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Hu C, Li S, Yang C, Chen J, Xiong Y, Fan G, Liu H, Hong L. ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. J Cheminform 2023; 15:91. [PMID: 37794460 PMCID: PMC10548653 DOI: 10.1186/s13321-023-00766-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023] Open
Abstract
In recent years, drug design has been revolutionized by the application of deep learning techniques, and molecule generation is a crucial aspect of this transformation. However, most of the current deep learning approaches do not explicitly consider and apply scaffold hopping strategy when performing molecular generation. In this work, we propose ScaffoldGVAE, a variational autoencoder based on multi-view graph neural networks, for scaffold generation and scaffold hopping of drug molecules. The model integrates several important components, such as node-central and edge-central message passing, side-chain embedding, and Gaussian mixture distribution of scaffolds. To assess the efficacy of our model, we conduct a comprehensive evaluation and comparison with baseline models based on seven general generative model evaluation metrics and four scaffold hopping generative model evaluation metrics. The results demonstrate that ScaffoldGVAE can explore the unseen chemical space and generate novel molecules distinct from known compounds. Especially, the scaffold hopped molecules generated by our model are validated by the evaluation of GraphDTA, LeDock, and MM/GBSA. The case study of generating inhibitors of LRRK2 for the treatment of PD further demonstrates the effectiveness of ScaffoldGVAE in generating novel compounds through scaffold hopping. This novel approach can also be applied to other protein targets of various diseases, thereby contributing to the future development of new drugs. Source codes and data are available at https://github.com/ecust-hc/ScaffoldGVAE .
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Affiliation(s)
- Chao Hu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Song Li
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Chenxing Yang
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Jun Chen
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China
| | - Guisheng Fan
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Hao Liu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China.
| | - Liang Hong
- School of Physics and Astronomy and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China.
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152
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Gan Y, Huang X, Guo W, Yan C, Zou G. Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor. Bioinformatics 2023; 39:btad607. [PMID: 37812255 PMCID: PMC10598574 DOI: 10.1093/bioinformatics/btad607] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/29/2023] [Accepted: 10/07/2023] [Indexed: 10/10/2023] Open
Abstract
MOTIVATION Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems. RESULTS Here, we propose DGSSynADR, a new deep learning method based on global structured features of drugs and targets for predicting synergistic anticancer drug combinations. DGSSynADR constructs a heterogeneous graph by integrating the drug-drug, drug-target, protein-protein interactions and multi-omics data, utilizes a low-rank global attention (LRGA) model to perform global weighted aggregation of graph nodes and learn the global structured features of drugs and targets, and then feeds the embedded features into a bilinear predictor to predict the synergy scores of drug combinations in different cancer cell lines. Specifically, LRGA network brings better model generalization ability, and effectively reduces the complexity of graph computation. The bilinear predictor facilitates the dimension transformation of the features and fuses the feature representation of the two drugs to improve the prediction performance. The loss function Smooth L1 effectively avoids gradient explosion, contributing to better model convergence. To validate the performance of DGSSynADR, we compare it with seven competitive methods. The comparison results demonstrate that DGSSynADR achieves better performance. Meanwhile, the prediction of DGSSynADR is validated by previous findings in case studies. Furthermore, detailed ablation studies indicate that the one-hot coding drug feature, LRGA model and bilinear predictor play a key role in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION DGSSynADR is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/DGSSynADR.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai 201600, China
| | - Xingyu Huang
- School of Computer Science and Technology, Donghua University, Shanghai 201600, China
| | - Wenjing Guo
- School of Computer Science and Technology, Donghua University, Shanghai 201600, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, Shanghai 201600, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
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153
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Emfinger CH, Clark LE, Yandell B, Schueler KL, Simonett SP, Stapleton DS, Mitok KA, Merrins MJ, Keller MP, Attie AD. Novel regulators of islet function identified from genetic variation in mouse islet Ca 2+ oscillations. eLife 2023; 12:RP88189. [PMID: 37787501 PMCID: PMC10547476 DOI: 10.7554/elife.88189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023] Open
Abstract
Insufficient insulin secretion to meet metabolic demand results in diabetes. The intracellular flux of Ca2+ into β-cells triggers insulin release. Since genetics strongly influences variation in islet secretory responses, we surveyed islet Ca2+ dynamics in eight genetically diverse mouse strains. We found high strain variation in response to four conditions: (1) 8 mM glucose; (2) 8 mM glucose plus amino acids; (3) 8 mM glucose, amino acids, plus 10 nM glucose-dependent insulinotropic polypeptide (GIP); and (4) 2 mM glucose. These stimuli interrogate β-cell function, α- to β-cell signaling, and incretin responses. We then correlated components of the Ca2+ waveforms to islet protein abundances in the same strains used for the Ca2+ measurements. To focus on proteins relevant to human islet function, we identified human orthologues of correlated mouse proteins that are proximal to glycemic-associated single-nucleotide polymorphisms in human genome-wide association studies. Several orthologues have previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1, and GCK), supporting our mouse-to-human integration as a discovery platform. By integrating these data, we nominate novel regulators of islet Ca2+ oscillations and insulin secretion with potential relevance for human islet function. We also provide a resource for identifying appropriate mouse strains in which to study these regulators.
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Affiliation(s)
| | - Lauren E Clark
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Brian Yandell
- Department of Statistics, University of Wisconsin-MadisonMadisonUnited States
| | - Kathryn L Schueler
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Shane P Simonett
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Donnie S Stapleton
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Kelly A Mitok
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Matthew J Merrins
- Department of Medicine, Division of Endocrinology, University of Wisconsin-MadisonMadisonUnited States
- William S. Middleton Memorial Veterans HospitalMadisonUnited States
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Medicine, Division of Endocrinology, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemistry, University of Wisconsin-MadisonMadisonUnited States
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154
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Riedl M, Mukherjee S, Gauthier M. Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma. Mol Pharm 2023; 20:4984-4993. [PMID: 37656906 DOI: 10.1021/acs.molpharmaceut.3c00129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Chemical-specific parameters are either measured in vitro or estimated using quantitative structure-activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work, we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers, which allowed us to circumvent the need for calculation of these chemical descriptors. In this approach, simplified molecular-input line-entry system (SMILES) strings were embedded in a high-dimensional space using a two-stage training approach. The model was first pre-trained on a masked SMILES token task and then fine-tuned on a QSAR prediction task. The pre-training task learned meaningful high-dimensional embeddings based upon the relationships between the chemical tokens in the SMILES strings derived from the "in-stock" portion of the ZINC 15 dataset─a large dataset of commercially available chemicals. The fine-tuning task then perturbed the pre-trained embeddings to facilitate prediction of a specific QSAR endpoint of interest. The power of this model stems from the ability to reuse the pre-trained model for multiple different fine-tuning tasks, reducing the computational burden of developing multiple models for different endpoints. We used our framework to develop a predictive model for fraction unbound in human plasma (fu,p). This approach is flexible, requires minimum domain expertise, and can be generalized for other parameters of interest for rapid and accurate estimation of absorption, distribution, metabolism, excretion, and toxicity.
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155
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Zaib S, Akram F, Waris W, Liaqat ST, Zaib Z, Khan I, Dera AA, Pashameah RA, Alzahrani E, Farouk AE. Computational approaches for innovative anti-viral drug discovery using Orthosiphon aristatus blume miq against dengue virus. J Biomol Struct Dyn 2023; 41:8738-8750. [PMID: 36300501 DOI: 10.1080/07391102.2022.2137238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 10/11/2022] [Indexed: 10/31/2022]
Abstract
Dengue virus has emerged as infectious mosquito borne disease involved in lowering platelets and white blood cells (WBC) count particularly. The genome structure is based on several structural and non-structural proteins essential for viral replication and progeny. One of the major proteins of replication is non-structural protein 3 (NS3) that transforms polyproteins into functional proteins with a cofactor non-structural protein (NS2B). Heat Shock Protein 70 (HSP70), is a human protein that assists in replication, viral entry and virion synthesis. Therefore, to inhibit the spread of dengue infection, there is a need of antivirals targeting replication proteins and other human proteins that help in dengue virus multiplication. By systemic approach based on molecular docking, ADMET (absorption, distribution, metabolism, excretion and toxicity) properties and molecular dynamic simulation (MD), potent inhibitors can be predicted. Inhibition of NS2B/NS3 dengue and HSP70 proteins involved in multiple steps in dengue virus progression can be prevented by using different phytochemicals. Molecular docking was performed using AutoDock Vina, PatchDock, and SwissDock. Interactions of obtained complex were observed in PyMOL and PLIP. Validation was checked by PROCHEK, simulation was performed using iMODS followed by preclinical testing by admetSAR. Ladanein, a flavonoid of Orthosiphon aristatus, was obtained as the lead compound to inhibit major replication protein of dengue virus with inhibitory potential against HSP70 protein. In summary, various in silico approaches were used to obtain the best phytochemical having anti-dengue potential.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Fatima Akram
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Wania Waris
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Syed Talha Liaqat
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Zainab Zaib
- Combined Military Hospital Abbottabad, Abbottabad, Pakistan
| | - Imtiaz Khan
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Ayed A Dera
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Rami Adel Pashameah
- Department of Chemistry, Faculty of Applied Science, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Eman Alzahrani
- Department of Chemistry, College of Science, Taif University, Taif, Saudi Arabia
| | - Abd-ElAziem Farouk
- Department of Biotechnology, College of Science, Taif University, Taif, Saudi Arabia
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156
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Haroon S, C A H, A S J. Generative Pre-trained Transformer (GPT) based model with relative attention for de novo drug design. Comput Biol Chem 2023; 106:107911. [PMID: 37450999 DOI: 10.1016/j.compbiolchem.2023.107911] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
De novo drug design refers to the process of designing new drug molecules from scratch using computational methods. In contrast to other computational methods that primarily focus on modifying existing molecules, designing from scratch enables the exploration of new chemical space and the potential discovery of novel molecules with enhanced properties. In this research, we proposed a model that utilizes Generative Pre-trained Transformer (GPT) architecture and relative attention for de novo drug design. GPT is a language model that utilizes transformer architecture to predict the next word or token in a given sequence. Representation of molecules using SMILES notation has enabled the use of next-token prediction techniques in de novo drug design. GPT uses attention mechanisms to capture the dependencies and relationships between different tokens in a sequence and allows the model to focus on the most important information when processing the input. Relative attention is a variant of the attention mechanism, which allows the model to capture the relative distances and relationships between tokens in the input sequence. In the standard attention mechanism, positional information is typically encoded using fixed-position embeddings. In relative attention, positional information is supplied dynamically during attention calculation by incorporating relative positional encodings, enabling the model to quickly learn the syntax of new unseen tokens. Relative attention enables the GPT model to better understand the relative positions of tokens in the sequence, which can be particularly useful when dealing with limited dataset sizes or generating target-specific drugs. The proposed model was trained on benchmark datasets, and performance was compared with other generative models. We show that relative attention and transfer learning could enable the GPT model to generate molecules with improved validity, uniqueness, and novelty in the context of de novo drug design. To illustrate the effectiveness of relative attention, the model was trained using transfer learning on three target-specific datasets, and the performance was compared with standard attention.
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Affiliation(s)
- Suhail Haroon
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
| | - Hafsath C A
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India
| | - Jereesh A S
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
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157
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Corvi JO, McKitrick A, Fernández JM, Fuenteslópez CV, Gelpí JL, Ginebra MP, Capella-Gutierrez S, Hakimi O. DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline. Adv Healthc Mater 2023; 12:e2300150. [PMID: 37563883 DOI: 10.1002/adhm.202300150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/04/2023] [Indexed: 08/12/2023]
Abstract
Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.
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Affiliation(s)
- Javier O Corvi
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | | | | | - Carla V Fuenteslópez
- Institute of Biomedical Engineering, Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - Josep L Gelpí
- Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, 08014, Spain
| | - Maria-Pau Ginebra
- Department of Materials Science and Engineering, The Technical University of Catalonia, 08222, Barcelona, Spain
| | | | - Osnat Hakimi
- Faculty of Medicine and Health Sciences, The International University of Catalonia, Barcelona, 08017, Spain
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158
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Peng W, Qi H, Zhu W, Tong L, Rouzi A, Wu Y, Han L, He L, Yan Y, Pan T, Liu J, Wang Q, Jia Z, Song Y, Zhu Q, Zhou J. Lianhua Qingke ameliorates lipopolysaccharide-induced lung injury by inhibiting neutrophil extracellular traps formation and pyroptosis. Pulm Circ 2023; 13:e12295. [PMID: 37808899 PMCID: PMC10557103 DOI: 10.1002/pul2.12295] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 10/10/2023] Open
Abstract
LHQK is a patented Traditional Chinese Medicine (TCM) which is clinically used for acute tracheobronchitis, cough, and other respiratory diseases. Recent studies have proved that LHQK exhibits excellent clinical efficacy in the treatment of acute lung injury (ALI). However, the corresponding mechanisms remain largely unexplored. In this study, we investigated the effects and the underlying mechanisms of LHQK on lipopolysaccharide (LPS)-induced ALI in mice. The pathological examination, inflammatory cytokines assessments, and mucus secretion evaluation indicated that administration of LHQK ameliorated LPS-induced lung injury, and suppressed the secretion of Muc5AC and pro-inflammatory cytokines (IL-6, TNF-α, and IL-1β) in plasma and BALF. Furthermore, the results of cell-free DNA level showed that LHQK significantly inhibited LPS-induced NETs formation. Western blot revealed that LHQK effectively inhibited LPS-triggered pyroptosis in the lung. In addition, RNA-Seq data analysis, relatively bioinformatic analysis, and network pharmacology analysis revealed that LHQK and relative components may play multiple protective functions in LPS-induced ALI/acute respiratory distress syndrome (ARDS) by regulating multiple targets directly or indirectly related to NETs and pyroptosis. In conclusion, LHQK can effectively attenuate lung injury and reduce lung inflammation by inhibiting LPS-induced NETs formation and pyroptosis, which may be regulated directly or indirectly by active compounds of LHQK.
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Affiliation(s)
- Wenjun Peng
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Hui Qi
- Hebei Academy of Integrated Traditional Chinese and Western MedicineHebeiShijiazhuangChina
| | - Wensi Zhu
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Lin Tong
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Ainiwaer Rouzi
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Yuanyuan Wu
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Linxiao Han
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Ludan He
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Yu Yan
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Ting Pan
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Jie Liu
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Qin Wang
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
| | - Zhenhua Jia
- Hebei Academy of Integrated Traditional Chinese and Western MedicineHebeiShijiazhuangChina
| | - Yuanlin Song
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
- Shanghai Institute of Infectious Disease and BiosecurityFudan UniversityShanghaiChina
- Research Center for Chemical Injury, Emergency and Critical Medicine of Fudan UniversityFudan UniversityShanghaiChina
| | - Qiaoliang Zhu
- Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Jian Zhou
- Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan HospitalFudan UniversityShanghaiChina
- Shanghai Key Laboratory of Lung Inflammation and InjuryShanghaiChina
- Shanghai Institute of Infectious Disease and BiosecurityFudan UniversityShanghaiChina
- Research Center for Chemical Injury, Emergency and Critical Medicine of Fudan UniversityFudan UniversityShanghaiChina
- Key Laboratory of Chemical Injury, Emergency and Critical Medicine of Shanghai Municipal Health CommissionFudan UniversityShanghaiChina
- Center of Emergency and Critical Medicine in Jinshan Hospital of Fudan UniversityFudan UniversityShanghaiChina
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159
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Zhai J, Man VH, Ji B, Cai L, Wang J. Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism. Drug Discov Today 2023; 28:103728. [PMID: 37517604 PMCID: PMC10543639 DOI: 10.1016/j.drudis.2023.103728] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 08/01/2023]
Abstract
The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.
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Affiliation(s)
- Jingchen Zhai
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Viet Hoang Man
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Beihong Ji
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Lianjin Cai
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
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160
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Zhu Y, Li M, Wang H, Yang F, Pang X, Du R, Zhang J, Huang X. Genetically proxied antidiabetic drugs targets and stroke risk. J Transl Med 2023; 21:681. [PMID: 37777789 PMCID: PMC10544120 DOI: 10.1186/s12967-023-04565-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/22/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Previous studies have assessed the association between antidiabetic drugs and stroke risk, but the results are inconsistent. Mendelian randomization (MR) was used to assess effects of antidiabetic drugs on stroke risk. METHODS We selected blood glucose-lowering variants in genes encoding antidiabetic drugs targets from genome-wide association studies (GWAS). A two-sample MR and Colocalization analyses were applied to examine associations between antidiabetic drugs and the risk of stroke. For antidiabetic agents that had effect on stroke risk, an independent blood glucose GWAS summary data was used for further verification. RESULTS Genetic proxies for sulfonylureas targets were associated with reduced risk of any stroke (OR=0.062, 95% CI 0.013-0.295, P=4.65×10-4) and any ischemic stroke (OR=0.055, 95% CI 0.010-0.289, P=6.25×10-4), but not with intracranial hemorrhage. Colocalization supported shared casual variants for blood glucose with any stroke and any ischemic stroke within the encoding genes for sulfonylureas targets (KCNJ11 and ABCC8) (posterior probability>0.7). Furthermore, genetic variants in the targets of insulin/insulin analogues, glucagon-like peptide-1 analogues, thiazolidinediones, and metformin were not associated with the risk of any stroke, any ischemic stroke and intracranial hemorrhage. The association was consistent in the analysis of sulfonylureas with stroke risk using an independent blood glucose GWAS summary data. CONCLUSIONS Our findings showed that genetic proxies for sulfonylureas targets by lowering blood glucose were associated with a lower risk of any stroke and any ischemic stroke. The study might be of great significance to guide the selection of glucose-lowering drugs in individuals at high risk of stroke.
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Affiliation(s)
- Yahui Zhu
- Medical School of Chinese PLA, Beijing, China
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Mao Li
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Hongfen Wang
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Fei Yang
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xinyuan Pang
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
- College of Medicine, Nankai University, Tianjin, China
| | - Rongrong Du
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
- College of Medicine, Nankai University, Tianjin, China
| | - Jinghong Zhang
- Medical School of Chinese PLA, Beijing, China
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xusheng Huang
- Medical School of Chinese PLA, Beijing, China.
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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161
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Ghafoor N, Yildiz A. Targeting MDM2-p53 Axis through Drug Repurposing for Cancer Therapy: A Multidisciplinary Approach. ACS OMEGA 2023; 8:34583-34596. [PMID: 37779953 PMCID: PMC10536845 DOI: 10.1021/acsomega.3c03471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.
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Affiliation(s)
- Naeem
Abdul Ghafoor
- Department
of Molecular Biology and Genetics, Graduate School of Natural and
Applied Sciences, Mugla Sitki Kocman University, 48000 Mugla, Turkey
| | - Aysegul Yildiz
- Department
of Molecular Biology and Genetics, Graduate School of Natural and
Applied Sciences, Mugla Sitki Kocman University, 48000 Mugla, Turkey
- Department
of Molecular Biology and Genetics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey
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162
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Chen Y, Liu Y, Chen N, Jin Y, Yang R, Yao H, Kong DX. A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class. Nat Prod Rep 2023; 40:1464-1478. [PMID: 37070562 DOI: 10.1039/d2np00089j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.
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Affiliation(s)
- Yinliang Chen
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - Yi Liu
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - Nianhang Chen
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - Yuting Jin
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - Ruofei Yang
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - Hucheng Yao
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
| | - De-Xin Kong
- National Key Laboratory of Agricultural Microbiology, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
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163
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Turon G, Hlozek J, Woodland JG, Kumar A, Chibale K, Duran-Frigola M. First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa. Nat Commun 2023; 14:5736. [PMID: 37714843 PMCID: PMC10504240 DOI: 10.1038/s41467-023-41512-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 09/06/2023] [Indexed: 09/17/2023] Open
Abstract
Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting.
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Affiliation(s)
- Gemma Turon
- Ersilia Open Source Initiative, Cambridge, UK
| | - Jason Hlozek
- Department of Chemistry and Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Cape Town, South Africa
| | - John G Woodland
- Department of Chemistry and Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council Drug Discovery and Development Research Unit, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Ankur Kumar
- Ersilia Open Source Initiative, Cambridge, UK
| | - Kelly Chibale
- Department of Chemistry and Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Cape Town, South Africa.
- South African Medical Research Council Drug Discovery and Development Research Unit, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
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164
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Abdullah Z, Chee HY, Yusof R, Mohd Fauzi F. Finding Lead Compounds for Dengue Antivirals from a Collection of Old Drugs through In Silico Target Prediction and Subsequent In Vitro Validation. ACS OMEGA 2023; 8:32483-32497. [PMID: 37720780 PMCID: PMC10500654 DOI: 10.1021/acsomega.3c02607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/14/2023] [Indexed: 09/19/2023]
Abstract
Dengue virus (DENV) infection is one of the most widely spread flavivirus infections. Despite the fatality it could cause, no antiviral treatment is currently available to treat the disease. Hence, this study aimed to repurpose old drugs as novel DENV NS3 inhibitors. Ligand-based (L-B) and proteochemometric (PCM) prediction models were built using 62,354 bioactivity data to screen for potential NS3 inhibitors. Selected drugs were then subjected to the foci forming unit reduction assay (FFURA) and protease inhibition assay. Finally, molecular docking was performed to validate these results. The in silico studies revealed that both models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation in terms of its sensitivity (0.671). In the in vitro validation, all drugs (zileuton, trimethadione, and linalool) were able to moderately inhibit the viral activities at the highest concentration tested. Zileuton showed comparable results with linalool when tested at 2 mM against the DENV NS3 protease, with a reduction of protease activity at 17.89 and 18.42%, respectively. Two new compounds were also proposed through the combination of the selected drugs, which are ziltri (zilueton + trimethadione) and zilool (zileuton + linalool). The molecular docking study confirms the in vitro observations where all drugs and proposed compounds were able to achieve binding affinity ≥ -4.1 kcal/mol, with ziltri showing the highest affinity at -7.7 kcal/mol, surpassing the control, panduratin A. The occupation of both S1 and S2 subpockets of NS2B-NS3 may be essential and a reason for the lower binding energy shown by the proposed compounds compared to the screened drugs. Based on the results, this study provided five potential new lead compounds (ziltri, zilool, zileuton, linalool, and trimethadione) for DENV that could be modified further.
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Affiliation(s)
- Zafirah
Liyana Abdullah
- Department
of Pharmaceutical Life Sciences, Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia
| | - Hui-Yee Chee
- Department
of Medical Microbiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Rohana Yusof
- Department
of Molecular Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Fazlin Mohd Fauzi
- Department
of Pharmacology and Pharmaceutical Chemistry, Faculty of Pharmacy, UiTM Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia
- Collaborative
Drug Discovery Research, Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia
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165
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You CL, Lee SJ, Lee J, Vuong TA, Lee HY, Jeong SY, Alishir A, Walker AS, Bae GU, Kim KH, Kang JS. Inonotus obliquus upregulates muscle regeneration and augments function through muscle oxidative metabolism. Int J Biol Sci 2023; 19:4898-4914. [PMID: 37781506 PMCID: PMC10539711 DOI: 10.7150/ijbs.84970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023] Open
Abstract
Skeletal muscle wasting related to aging or pathological conditions is critically associated with the increased incidence and prevalence of secondary diseases including cardiovascular diseases, metabolic syndromes, and chronic inflammations. Much effort is made to develop agents to enhance muscle metabolism and function. Inonotus obliquus (I. obliquus; IO) is a mushroom popularly called chaga and has been widely employed as a folk medicine for inflammation, cardiovascular diseases, diabetes, and cancer in Eastern Europe and Asia. However, its effect on muscle health has not been explored. Here, we aimed to investigate the beneficial effect of IO extract in muscle regeneration and metabolism. The treatment of IO in C2C12 myoblasts led to increased myogenic differentiation and alleviation of dexamethasone-induced myotube atrophy. Network pharmacological analysis using the identified specific chemical constituents of IO extracts predicted protein kinase B (AKT)-dependent mechanisms to promote myogenesis and muscle regeneration. Consistently, IO treatment resulted in the activation of AKT, which suppressed muscle-specific ubiquitin E3 ligases induced by dexamethasone. IO treatment in mice improved the regeneration of cardiotoxin-injured muscles accompanied by elevated proliferation and differentiation of muscle stem cells. Furthermore, it elevated the mitochondrial content and muscle oxidative metabolism accompanied by the induction of peroxisome proliferator-activated receptor γ coactivator α (PGC-1α). Our current data suggest that IO is a promising natural agent in enhancing muscle regenerative capacity and oxidative metabolism thereby preventing muscle wasting.
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Affiliation(s)
- Chang-Lim You
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Sang-Jin Lee
- Research Institute of Aging Related Disease, AniMusCure Inc., Suwon 16419, Republic of Korea
| | - Jinwoo Lee
- Research Institute of Aging Related Disease, AniMusCure Inc., Suwon 16419, Republic of Korea
| | - Tuan Anh Vuong
- Research Institute of Aging Related Disease, AniMusCure Inc., Suwon 16419, Republic of Korea
| | - Hye-Young Lee
- Research Institute of Aging Related Disease, AniMusCure Inc., Suwon 16419, Republic of Korea
| | - Se Yun Jeong
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Akida Alishir
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Allison S. Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, United States
| | - Gyu-Un Bae
- Research Institute of Aging Related Disease, AniMusCure Inc., Suwon 16419, Republic of Korea
- Drug Information Research Institute, Muscle Physiome Research Center, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Ki Hyun Kim
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jong-Sun Kang
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
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166
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Monteiro NRC, Pereira TO, Machado ACD, Oliveira JL, Abbasi M, Arrais JP. FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers. Comput Biol Med 2023; 164:107285. [PMID: 37557054 DOI: 10.1016/j.compbiomed.2023.107285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
The design of compounds that target specific biological functions with relevant selectivity is critical in the context of drug discovery, especially due to the polypharmacological nature of most existing drug molecules. In recent years, in silico-based methods combined with deep learning have shown promising results in the de novo drug design challenge, leading to potential leads for biologically interesting targets. However, several of these methods overlook the importance of certain properties, such as validity rate and target selectivity, or simplify the generative process by neglecting the multi-objective nature of the pharmacological space. In this study, we propose a multi-objective Transformer-based architecture to generate drug candidates with desired molecular properties and increased selectivity toward a specific biological target. The framework consists of a Transformer-Decoder Generator that generates novel and valid compounds in the SMILES format notation, a Transformer-Encoder Predictor that estimates the binding affinity toward the biological target, and a feedback loop combined with a multi-objective optimization strategy to rank the generated molecules and condition the generating distribution around the targeted properties. The results demonstrate that the proposed architecture can generate novel and synthesizable small compounds with desired pharmacological properties toward a biologically relevant target. The unbiased Transformer-based Generator achieved superior performance in the novelty rate (97.38%) and comparable performance in terms of internal diversity, uniqueness, and validity against state-of-the-art baselines. The optimization of the unbiased Transformer-based Generator resulted in the generation of molecules exhibiting high binding affinity toward the Adenosine A2A Receptor (AA2AR) and possessing desirable physicochemical properties, where 99.36% of the generated molecules follow Lipinski's rule of five. Furthermore, the implementation of a feedback strategy, in conjunction with a multi-objective algorithm, effectively shifted the distribution of the generated molecules toward optimal values of molecular weight, molecular lipophilicity, topological polar surface area, synthetic accessibility score, and quantitative estimate of drug-likeness, without the necessity of prior training sets comprising molecules endowed with pharmacological properties of interest. Overall, this research study validates the applicability of a Transformer-based architecture in the context of drug design, capable of exploring the vast chemical representation space to generate novel molecules with improved pharmacological properties and target selectivity. The data and source code used in this study are available at: https://github.com/larngroup/FSM-DDTR.
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Affiliation(s)
- Nelson R C Monteiro
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
| | - Tiago O Pereira
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
| | - Ana Catarina D Machado
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
| | - José L Oliveira
- IEETA, Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal.
| | - Maryam Abbasi
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal; Polytechnic Institute of Coimbra, Applied Research Institute, Coimbra, Portugal.
| | - Joel P Arrais
- University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
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167
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Martin HJ, Melo-Filho CC, Korn D, Eastman RT, Rai G, Simeonov A, Zakharov AV, Muratov E, Tropsha A. Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules. Antiviral Res 2023; 217:105620. [PMID: 37169224 PMCID: PMC11069349 DOI: 10.1016/j.antiviral.2023.105620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/13/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023]
Abstract
Diseases caused by new viruses cost thousands if not millions of human lives and trillions of dollars. We have identified, collected, curated, and integrated all chemogenomics data from ChEMBL for 13 emerging viruses that hold the greatest potential threat to global human health. By identifying and solving several challenges related to data annotation accuracy, we developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic and target-based assays for these viruses that we dubbed SMACC (Small Molecule Antiviral Compound Collection). The pilot version of the SMACC database contains over 32,500 entries for 13 viruses. By analyzing data in SMACC, we have identified ∼50 compounds with polyviral inhibition profile, mostly covering flavi- and coronaviruses. The SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks. SMACC is publicly available at https://smacc.mml.unc.edu.
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Affiliation(s)
- Holli-Joi Martin
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Cleber C Melo-Filho
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Daniel Korn
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Richard T Eastman
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, 20850, USA
| | - Ganesha Rai
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, 20850, USA
| | - Anton Simeonov
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, 20850, USA
| | - Alexey V Zakharov
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, 20850, USA.
| | - Eugene Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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168
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Hartley M, Iudin A, Padwardhan A, Sarkans U, Yoldaş AK, Kleywegt GJ. Providing open imaging data at scale: An EMBL-EBI perspective. Histochem Cell Biol 2023; 160:211-221. [PMID: 37537341 PMCID: PMC10492673 DOI: 10.1007/s00418-023-02216-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 08/05/2023]
Abstract
Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.
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Affiliation(s)
- Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ardan Padwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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169
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Dabburu GR, Jain A, Subbarao N, Kumar M. Designing dual inhibitors against potential drug targets of Plasmodium falciparum -M17 Leucyl Aminopeptidase and Plasmepsins. J Biomol Struct Dyn 2023; 41:8026-8041. [PMID: 36214679 DOI: 10.1080/07391102.2022.2129452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/17/2022] [Indexed: 10/17/2022]
Abstract
Malaria is one of the major diseases of concern worldwide, especially in the African regions. According to a recent WHO report, 95% of deaths that occur due to malaria are in the African regions. Resistance to present antimalarial drugs is increasing rapidly and becoming a problem of concern. M17 Leucyl Aminopeptidase (PfM17LAP) and vacuolar Plasmepsins (PfPM) are two important enzymes involved in the haemoglobin degradation pathway of Plasmodium falciparum. PfM17LAP regulates the release of amino acids and PfPM mediates the conversion of haemoglobin proteins to oligopeptides. These enzymes thus play an essential role in the survival of malaria parasites inside the human body. In the present study, we used in-silico molecular docking, simulation and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) studies to find potential dual inhibitors of PfPM and PfM17LAP using the ChEMBL antimalarial library. Absorption, distribution, metabolism, excretion and toxicity (ADMET) profiling of the top ten ranked molecules was done using the BIOVIA Discovery Studio. The present investigation revealed that the compound CHEMBL426945 is stable in the binding site of both PfPM and PfM17LAP. In this study, we have reported novel dual-inhibitors that may act better than the present antimalarial drugs.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Govinda Rao Dabburu
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Aakriti Jain
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Naidu Subbarao
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Manish Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
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170
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Croft D, Lodhia P, Lourenco S, MacKay C. Effectively utilizing publicly available databases for cancer target evaluation. NAR Cancer 2023; 5:zcad035. [PMID: 37457379 PMCID: PMC10346432 DOI: 10.1093/narcan/zcad035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/12/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available, how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.
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Affiliation(s)
- Daniel Croft
- Cancer Research Horizons, The Cancer Research UK Beatson Institute, Glasgow, G61 1BD, UK
| | - Puja Lodhia
- Cancer Research Horizons, The Francis Crick Institute, London, NW1 1AT, UK
| | - Sofia Lourenco
- Cancer Research Horizons, The Francis Crick Institute, London, NW1 1AT, UK
| | - Craig MacKay
- Cancer Research Horizons, The Cancer Research UK Beatson Institute, Glasgow, G61 1BD, UK
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171
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Wang Z, Sun L, Xu Y, Liang P, Xu K, Huang J. Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation. J Transl Med 2023; 21:579. [PMID: 37641144 PMCID: PMC10464202 DOI: 10.1186/s12967-023-04443-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Janus kinase 1 (JAK1) plays a critical role in most cytokine-mediated inflammatory, autoimmune responses and various cancers via the JAK/STAT signaling pathway. Inhibition of JAK1 is therefore an attractive therapeutic strategy for several diseases. Recently, high-performance machine learning techniques have been increasingly applied in virtual screening to develop new kinase inhibitors. Our study aimed to develop a novel layered virtual screening method based on machine learning (ML) and pharmacophore models to identify the potential JAK1 inhibitors. METHODS Firstly, we constructed a high-quality dataset comprising 3834 JAK1 inhibitors and 12,230 decoys, followed by establishing a series of classification models based on a combination of three molecular descriptors and six ML algorithms. To further screen potential compounds, we constructed several pharmacophore models based on Hiphop and receptor-ligand algorithms. We then used molecular docking to filter the recognized compounds. Finally, the binding stability and enzyme inhibition activity of the identified compounds were assessed by molecular dynamics (MD) simulations and in vitro enzyme activity tests. RESULTS The best performance ML model DNN-ECFP4 and two pharmacophore models Hiphop3 and 6TPF 08 were utilized to screen the ZINC database. A total of 13 potentially active compounds were screened and the MD results demonstrated that all of the above molecules could bind with JAK1 stably in dynamic conditions. Among the shortlisted compounds, the four purchasable compounds demonstrated significant kinase inhibition activity, with Z-10 being the most active (IC50 = 194.9 nM). CONCLUSION The current study provides an efficient and accurate integrated model. The hit compounds were promising candidates for the further development of novel JAK1 inhibitors.
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Affiliation(s)
- Zixiao Wang
- Department of Pharmacy, Honghui Hospital, Xi' an Jiaotong University, Xi' an, 710054, China.
| | - Lili Sun
- Department of Pharmacy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yu Xu
- State Key Laboratory of Natural Medicines,Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases, Center of Drug Discovery,China Pharmaceutical University, Nanjing, 210009, China
| | - Peida Liang
- Department of Pharmacy, Honghui Hospital, Xi' an Jiaotong University, Xi' an, 710054, China
| | - Kaiyan Xu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Jing Huang
- Department of Pharmacy, Honghui Hospital, Xi' an Jiaotong University, Xi' an, 710054, China.
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172
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Bae CH, Kim HY, Seo JE, Lee H, Kim S. In Silico Analysis of Pyeongwi-San Involved in Inflammatory Bowel Disease Treatment Using Network Pharmacology, Molecular Docking, and Molecular Dynamics. Biomolecules 2023; 13:1322. [PMID: 37759722 PMCID: PMC10526905 DOI: 10.3390/biom13091322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGOUND Pyeongwi-san (PWS) is a widely used formula for treating digestive disorders in Korea and China. Inflammatory bowel disease (IBD) is characterized by progressive inflammation of the gastrointestinal tract. Emerging evidence supports the protective effect of PWS against IBD, but specific mechanisms are still elusive. METHODS Active compounds of PWS were screened from the medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC) in the consideration of drug-likeness and oral bioavailability. Target candidates of active compounds were predicted using the ChEMBL database. IBD-related targets were obtained from the GeneCards and DisGeNET databases. The network of composition-targets-disease was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Molecular docking was used to simulate the binding affinity of active compounds on target proteins and molecular dynamics was used to validate the molecular docking result. RESULTS A total of 26 core target proteins of PWS were related to IBD. Enrichment analysis suggested that PWS is highly associated with tumor necrosis factor signaling pathway, apoptosis, and the collapse of tight junctions. Moreover, molecular docking and molecular dynamics simulation proposed β-eudesmol and (3R,6R,7S)-1,10-bisaboladien-3-ol to ameliorate IBD through the binding to TNF and MMP9, respectively. CONCLUSION Present in silico analysis revealed potential pathways and insight of PWS to regulate IBD. These results imply that the therapeutic effect of PWS might be achieved via an inhibitory effect.
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Affiliation(s)
- Chang-Hwan Bae
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Hee-Young Kim
- Korean Medicine Research Center for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea;
| | - Ji Eun Seo
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Hanul Lee
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
| | - Seungtae Kim
- Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (C.-H.B.); (J.E.S.); (H.L.)
- Korean Medicine Research Center for Healthy Aging, Pusan National University, Yangsan 50612, Republic of Korea;
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173
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Yi J, Lee S, Lim S, Cho C, Piao Y, Yeo M, Kim D, Kim S, Lee S. Exploring chemical space for lead identification by propagating on chemical similarity network. Comput Struct Biotechnol J 2023; 21:4187-4195. [PMID: 37680266 PMCID: PMC10480321 DOI: 10.1016/j.csbj.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/08/2023] [Accepted: 08/20/2023] [Indexed: 09/09/2023] Open
Abstract
Motivation Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.
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Affiliation(s)
- Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sangsoo Lim
- School of AI Software Convergence, Dongguk University, Pildong-ro 1-gil, Jung-gu, Seoul, South Korea
| | - Changyun Cho
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Marie Yeo
- PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea
| | - Dongkyu Kim
- PHARMGENSCIENCE CO., LTD., 216, Dongjak-daero, Seocho-gu, Seoul, 06554, South Korea
| | - Sun Kim
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
- AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
| | - Sunho Lee
- AIGENDRUG CO., LTD., Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea
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174
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Ley M, Heinzel A, Fillinger L, Kratochwill K, Perco P. OntoloViz: a GUI for interactive visualization of ranked disease or drug lists using the MeSH and ATC ontologies. BIOINFORMATICS ADVANCES 2023; 3:vbad113. [PMID: 38496343 PMCID: PMC10941809 DOI: 10.1093/bioadv/vbad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 03/19/2024]
Abstract
Motivation Structured vocabularies for drugs and diseases represent, besides their primary use for annotating scientific literature or scientific information in general, a valuable resource for visualizing aggregated information. The Medical Subject Headings (MeSH) and Anatomical Therapeutic Chemical (ATC) ontologies are widely used structured vocabularies for diseases and drugs, respectively. Their hierarchical tree-like structure can be used as a basis for creating intuitive visual displays for specific diseases and drugs within their higher-order classifications. Such displays are helpful means to contextualize diseases and drugs in various settings such as in drug repositioning. However, there are few tools that can harness the potential of these structured ontologies to create informative visual representations without extensive programming and data processing skills. Results We have developed OntoloViz, a Graphical User Interface (GUI) for visualizing annotated lists of drugs or diseases in the context of their MeSH or ATC ontologies in an intuitively interpretable sunburst layout. Minimum input is a list of disease or drug names. Users in addition have the option to specify numerical parameters for the input lists to enhance the visualization, e.g. to visualize term frequencies. The GUI allows values to be propagated upwards in the respective ontology tree structure thus facilitating exploration of gene and drug lists. We present two use cases for OntoloViz, namely (i) a graphical representation of clinically tested drugs for coronavirus disease (COVID-19) based on ATC Classification and (ii) a graphical representation of literature annotation of human diseases on the MeSH ontology. Availability and implementation The OntoloViz package can be retrieved from PyPi. The source code along with test data, template, and documentations are available at GitHub (https://github.com/Delta4AI/OntoloViz).
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Affiliation(s)
- Matthias Ley
- Computational Biology Department, Delta4 GmbH, Vienna 1080, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University Vienna, Vienna 1090, Austria
| | - Andreas Heinzel
- Computational Biology Department, Delta4 GmbH, Vienna 1080, Austria
- Department of Internal Medicine III, Medical University Vienna, Vienna 1090, Austria
| | - Lucas Fillinger
- Computational Biology Department, Delta4 GmbH, Vienna 1080, Austria
| | - Klaus Kratochwill
- Computational Biology Department, Delta4 GmbH, Vienna 1080, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University Vienna, Vienna 1090, Austria
| | - Paul Perco
- Computational Biology Department, Delta4 GmbH, Vienna 1080, Austria
- Department of Internal Medicine IV, Medical University Innsbruck, Innsbruck 6020, Austria
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175
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Smajić A, Rami I, Sosnin S, Ecker GF. Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets. Chem Res Toxicol 2023; 36:1300-1312. [PMID: 37439496 PMCID: PMC10445286 DOI: 10.1021/acs.chemrestox.3c00042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Indexed: 07/14/2023]
Abstract
Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.
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Affiliation(s)
- Aljoša Smajić
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Iris Rami
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Sergey Sosnin
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
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176
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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177
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Goßen J, Ribeiro RP, Bier D, Neumaier B, Carloni P, Giorgetti A, Rossetti G. AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology. Chem Sci 2023; 14:8651-8661. [PMID: 37592985 PMCID: PMC10430665 DOI: 10.1039/d3sc02352d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Abstract
Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.
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Affiliation(s)
- Jonas Goßen
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Faculty of Mathematics, Computer Science and Natural Sciences RWTH Aachen University Aachen Germany
| | - Rui Pedro Ribeiro
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Dirk Bier
- Institute of Neuroscience and Medicine, Nuclear Chemistry (INM-5), Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Bernd Neumaier
- Institute of Neuroscience and Medicine, Nuclear Chemistry (INM-5), Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany
- Institute of Radiochemistry and Experimental Molecular Imaging, University of Cologne, Faculty of Medicine and University Hospital Cologne Kerpener Straße 62 50937 Cologne Germany
| | - Paolo Carloni
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Faculty of Mathematics, Computer Science and Natural Sciences RWTH Aachen University Aachen Germany
- JARA-Institut Molecular Neuroscience and Neuroimaging (INM-11) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
| | - Alejandro Giorgetti
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Department of Biotechnology University of Verona Verona Italy
| | - Giulia Rossetti
- Institute for Computational Biomedicine (INM-9/IAS-5) Forschungszentrum Jülich Wilhelm-Johnen-Straße 52428 Jülich Germany
- Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany
- Department of Neurology University Hospital Aachen (UKA), RWTH Aachen University Aachen Germany
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178
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Venkatraman V. FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools. Front Chem 2023; 11:1239467. [PMID: 37649967 PMCID: PMC10462816 DOI: 10.3389/fchem.2023.1239467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer's. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62-0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
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179
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Lanini J, Santarossa G, Sirockin F, Lewis R, Fechner N, Misztela H, Lewis S, Maziarz K, Stanley M, Segler M, Stiefl N, Schneider N. PREFER: A New Predictive Modeling Framework for Molecular Discovery. J Chem Inf Model 2023; 63:4497-4504. [PMID: 37487018 DOI: 10.1021/acs.jcim.3c00523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks and the large number of molecular representations make it difficult to properly evaluate, reproduce, and compare them. Here we present a new PREdictive modeling FramEwoRk for molecular discovery (PREFER), written in Python (version 3.7.7) and based on AutoSklearn (version 0.14.7), that allows comparison between different molecular representations and common machine-learning models. We provide an overview of the design of our framework and show exemplary use cases and results of several representation-model combinations on diverse data sets, both public and in-house. Finally, we discuss the use of PREFER on small data sets. The code of the framework is freely available on GitHub.
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Affiliation(s)
- Jessica Lanini
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Gianluca Santarossa
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Finton Sirockin
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Richard Lewis
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Nikolas Fechner
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | | | - Sarah Lewis
- Microsoft Research AI4Science, Cambridge CB1 2FB, U.K
| | | | - Megan Stanley
- Microsoft Research AI4Science, Cambridge CB1 2FB, U.K
| | - Marwin Segler
- Microsoft Research AI4Science, Cambridge CB1 2FB, U.K
| | - Nikolaus Stiefl
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Nadine Schneider
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
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180
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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181
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Pusparini RT, Krisnadhi AA, Firdayani. MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors. Molecules 2023; 28:5843. [PMID: 37570812 PMCID: PMC10421274 DOI: 10.3390/molecules28155843] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure-activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways.
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Affiliation(s)
- Rizki Triyani Pusparini
- Tokopedia-UI AI Center of Excellence, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
- Research Center for Vaccine and Drugs, Research Organization for Health, National Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia;
| | - Adila Alfa Krisnadhi
- Tokopedia-UI AI Center of Excellence, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
| | - Firdayani
- Research Center for Vaccine and Drugs, Research Organization for Health, National Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia;
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182
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Rabaan AA, Alwashmi ASS, Mashraqi MM, Alshehri AA, Alawfi A, Alshengeti A, Najim MA, AlShehail BM, AlShahrani AJ, Garout M. Cheminformatics and machine learning approaches for repurposing anti-viral compounds against monkeypox virus thymidylate kinase. Mol Divers 2023:10.1007/s11030-023-10705-8. [PMID: 37531040 DOI: 10.1007/s11030-023-10705-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023]
Abstract
One of the emerging epidemic concerns is Monkeypox disease which is spreading globally. This disease is caused by the monkeypox virus (MPXV), with an increasing global incidence with an outbreak in 2022. One of the novel targets for monkeypox disease is thymidylate kinase, which is involved in pyrimidine metabolism. In this study, docking-based virtual screening and molecular dynamics techniques were employed in addition to the machine learning (ML) model to investigate the potential anti-viral natural small compounds to inhibit thymidylate kinase of MPXV. Several potential hits were identified through high-throughput virtual screening, and further top three candidates were selected, which ranked using the ML model. These three compounds were then examined under molecular dynamics simulation and MM/GBSA-binding free energy analysis. Among these, Chlorhexidine HCl showed high potential for binding to the thymidylate kinase with stable and consistent conformation with RMSD < 0.3 nm. The MM/GBSA analysis also showed the minimum binding free energy (ΔGTOTAL) of -62.41 kcal/mol for this compound. Overall, this study used structure-based drug design complemented by machine learning-guided ligand-based drug design to screen potential hit compounds from the anti-viral natural compound database.
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Affiliation(s)
- Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, 31311, Dhahran, Saudi Arabia.
- College of Medicine, Alfaisal University, 11533, Riyadh, Saudi Arabia.
- Department of Public Health and Nutrition, The University of Haripur, Haripur, 22610, Pakistan.
| | - Ameen S S Alwashmi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, 51452, Buraydah, Saudi Arabia
| | - Mutaib M Mashraqi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, 61441, Najran, Saudi Arabia
| | - Ahmad A Alshehri
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, 61441, Najran, Saudi Arabia
| | - Abdulsalam Alawfi
- Department of Pediatrics, College of Medicine, Taibah University, 41491, Medina, Saudi Arabia
| | - Amer Alshengeti
- Department of Pediatrics, College of Medicine, Taibah University, 41491, Medina, Saudi Arabia
- Department of Infection prevention and control, Prince Mohammad Bin Abdulaziz Hospital, National Guard Health Affairs, 41491, Medina, Saudi Arabia
| | - Mustafa A Najim
- Department of Medical Laboratories Technology, College of Applied Medical Sciences, Taibah University, 41411, Medina, Saudi Arabia
| | - Bashayer M AlShehail
- Pharmacy Practice Department, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, 31441, Dammam, Saudi Arabia
| | - Abdullah J AlShahrani
- Department of Public Health, Health affairs, Ministry of Health, 62523, Asir, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, 21955, Mecca, Saudi Arabia.
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183
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Zhang Z, Xie A, Guan J, Zhou S. Molecular property prediction by semantic-invariant contrastive learning. Bioinformatics 2023; 39:btad462. [PMID: 37505457 PMCID: PMC10397537 DOI: 10.1093/bioinformatics/btad462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/07/2023] [Accepted: 07/27/2023] [Indexed: 07/29/2023] Open
Abstract
MOTIVATION Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. RESULTS To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. AVAILABILITY AND IMPLEMENTATION The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL.
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Affiliation(s)
- Ziqiao Zhang
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
| | - Ailin Xie
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
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184
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Papastergiou T, Azé J, Bringay S, Louet M, Poncelet P, Rosales-Hurtado M, Vo-Hoang Y, Licznar-Fajardo P, Docquier JD, Gavara L. Discovering NDM-1 inhibitors using molecular substructure embeddings representations. J Integr Bioinform 2023; 0:jib-2022-0050. [PMID: 37498676 PMCID: PMC10389050 DOI: 10.1515/jib-2022-0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds.
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Affiliation(s)
- Thomas Papastergiou
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | - Jérôme Azé
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
| | - Sandra Bringay
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
- AMIS, Paul Valery University, 34199 Montpellier, France
| | - Maxime Louet
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | - Pascal Poncelet
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
| | | | - Yen Vo-Hoang
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | | | - Jean-Denis Docquier
- Department of Medical Biotechnologies, University of Siena, I-53100 Siena, Italy
| | - Laurent Gavara
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
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185
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Ohue M, Kojima Y, Kosugi T. Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties. Molecules 2023; 28:5652. [PMID: 37570623 PMCID: PMC10420264 DOI: 10.3390/molecules28155652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.
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Affiliation(s)
- Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa 226-8501, Japan (T.K.)
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186
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Lamens A, Bajorath J. Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values. Molecules 2023; 28:5601. [PMID: 37513472 PMCID: PMC10383571 DOI: 10.3390/molecules28145601] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Most machine learning (ML) models produce black box predictions that are difficult, if not impossible, to understand. In pharmaceutical research, black box predictions work against the acceptance of ML models for guiding experimental work. Hence, there is increasing interest in approaches for explainable ML, which is a part of explainable artificial intelligence (XAI), to better understand prediction outcomes. Herein, we have devised a test system for the rationalization of multiclass compound activity prediction models that combines two approaches from XAI for feature relevance or importance analysis, including counterfactuals (CFs) and Shapley additive explanations (SHAP). For compounds with different single- and dual-target activities, we identified small compound modifications that induce feature changes inverting class label predictions. In combination with feature mapping, CFs and SHAP value calculations provide chemically intuitive explanations for model decisions.
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Affiliation(s)
- Alec Lamens
- Department of Life Science Informatics, B-IT, LIMES Program, Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program, Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
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187
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Mittal RK, Purohit P, Sankaranarayanan M, Muzaffar-Ur-Rehman M, Taramelli D, Signorini L, Dolci M, Basilico N. In-vitro antiviral activity and in-silico targeted study of quinoline-3-carboxylate derivatives against SARS-Cov-2 isolate. Mol Divers 2023:10.1007/s11030-023-10703-w. [PMID: 37480422 DOI: 10.1007/s11030-023-10703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
In recent years, the viral outbreak named COVID-19 showed that infectious diseases have a huge impact on both global health and the financial and economic sectors. The lack of efficacious antiviral drugs worsened the health problem. Based on our previous experience, we investigated in vitro and in silico a series of quinoline-3-carboxylate derivatives against a SARS-CoV-2 isolate. In the present study, the in-vitro antiviral activity of a series of quinoline-3-carboxylate compounds and the in silico target-based molecular dynamics (MD) and metabolic studies are reported. The compounds' activity against SARS-CoV-2 was evaluated using plaque assay and RT-qPCR. Moreover, from the docking scores, it appears that the most active compounds (1j and 1o) exhibit stronger binding affinity to the primary viral protease (NSP5) and the exoribonuclease domain of non structural protein 14 (NSP14). Additionally, the in-silico metabolic analysis of 1j and 1o defines CYP2C9 and CYP3A4 as the major P450 enzymes involved in their metabolism.
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Affiliation(s)
- Ravi Kumar Mittal
- National Institute of Pharmaceutical Education and Research, S A S Nagar Mohali, Punjab, 160062, India
- Galgotias College of Pharmacy, Greater Noida, UttarPradesh, India
| | - Priyank Purohit
- School of Pharmacy, Graphic Era Hill University, Dehradun, Uttarakhand, 248002, India.
| | - Murugesan Sankaranarayanan
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India
| | - Mohammed Muzaffar-Ur-Rehman
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India
| | - Donatella Taramelli
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Lucia Signorini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Maria Dolci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Nicoletta Basilico
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
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188
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Athanasiadis P, Ravikumar B, Elliott RJ, Dawson JC, Carragher NO, Clemons PA, Johanssen T, Ebner D, Aittokallio T. Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells. iScience 2023; 26:107209. [PMID: 37485377 PMCID: PMC10359939 DOI: 10.1016/j.isci.2023.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
| | - Richard J.R. Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C. Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O. Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Paul A. Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, United States
| | - Timothy Johanssen
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel Ebner
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tero Aittokallio
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
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189
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Szulc NA, Mackiewicz Z, Bujnicki JM, Stefaniak F. Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. Brief Bioinform 2023; 24:bbad187. [PMID: 37204195 DOI: 10.1093/bib/bbad187] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.
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Affiliation(s)
- Natalia A Szulc
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
- Laboratory of Protein Metabolism, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
| | - Zuzanna Mackiewicz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
- Laboratory of RNA Biology - ERA Chairs Group, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, 4 Ks. Trojdena Str, 02-109 Warsaw, Poland
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190
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Chambers RK, Weaver JD, Kim J, Hoar JL, Krska SW, White MC. A preparative small-molecule mimic of liver CYP450 enzymes in the aliphatic C-H oxidation of carbocyclic N-heterocycles. Proc Natl Acad Sci U S A 2023; 120:e2300315120. [PMID: 37428920 PMCID: PMC10629554 DOI: 10.1073/pnas.2300315120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023] Open
Abstract
An emerging trend in small-molecule pharmaceuticals, generally composed of nitrogen heterocycles (N-heterocycles), is the incorporation of aliphatic fragments. Derivatization of the aliphatic fragments to improve drug properties or identify metabolites often requires lengthy de novo syntheses. Cytochrome P450 (CYP450) enzymes are capable of direct site- and chemo-selective oxidation of a broad range of substrates but are not preparative. A chemoinformatic analysis underscored limited structural diversity of N-heterocyclic substrates oxidized using chemical methods relative to pharmaceutical chemical space. Here, we describe a preparative chemical method for direct aliphatic oxidation that tolerates a wide range of nitrogen functionality (chemoselective) and matches the site of oxidation (site-selective) of liver CYP450 enzymes. Commercial small-molecule catalyst Mn(CF3-PDP) selectively effects direct methylene oxidation in compounds bearing 25 distinct heterocycles including 14 out of 27 of the most frequent N-heterocycles found in U.S. Food and Drug Administration (FDA)-approved drugs. Mn(CF3-PDP) oxidations of carbocyclic bioisostere drug candidates (for example, HCV NS5B and COX-2 inhibitors including valdecoxib and celecoxib derivatives) and precursors of antipsychotic drugs blonanserin, buspirone, and tiospirone and the fungicide penconazole are demonstrated to match the major site of aliphatic metabolism obtained with liver microsomes. Oxidations are demonstrated at low Mn(CF3-PDP) loadings (2.5 to 5 mol%) on gram scales of substrate to furnish preparative amounts of oxidized products. A chemoinformatic analysis supports that Mn(CF3-PDP) significantly expands the pharmaceutical chemical space accessible to small-molecule C-H oxidation catalysis.
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Affiliation(s)
- Rachel K. Chambers
- Department of Chemistry, Roger Adams Laboratory, University of Illinois, Urbana, IL61801
| | - Jacob D. Weaver
- Department of Chemistry, Roger Adams Laboratory, University of Illinois, Urbana, IL61801
| | - Jinho Kim
- Department of Chemistry, Roger Adams Laboratory, University of Illinois, Urbana, IL61801
| | - Jason L. Hoar
- Department of Discovery Chemistry, Merck & Co., Inc., Rahway, NJ07065
| | - Shane W. Krska
- Department of Discovery Chemistry, Merck & Co., Inc., Rahway, NJ07065
| | - M. Christina White
- Department of Chemistry, Roger Adams Laboratory, University of Illinois, Urbana, IL61801
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191
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Chen L, Fan Z, Chang J, Yang R, Hou H, Guo H, Zhang Y, Yang T, Zhou C, Sui Q, Chen Z, Zheng C, Hao X, Zhang K, Cui R, Zhang Z, Ma H, Ding Y, Zhang N, Lu X, Luo X, Jiang H, Zhang S, Zheng M. Sequence-based drug design as a concept in computational drug design. Nat Commun 2023; 14:4217. [PMID: 37452028 PMCID: PMC10349078 DOI: 10.1038/s41467-023-39856-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.
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Affiliation(s)
- Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zisheng Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
| | - Jie Chang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Ruirui Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
| | - Hui Hou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Hao Guo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Yinghui Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Tianbiao Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Chenmao Zhou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Qibang Sui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zhengyang Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Chen Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xinyue Hao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Keke Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
| | - Rongrong Cui
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hudson Ma
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Yiluan Ding
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Naixia Zhang
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xiaojie Lu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Sub-lane Xiangshan, Hangzhou, 310024, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, 138 Xianlin Road, Jiangsu, Nanjing, 210023, China.
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, No. 393 Huaxia Middle Road, Shanghai, 200031, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Sub-lane Xiangshan, Hangzhou, 310024, China.
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192
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Sadler MC, Auwerx C, Deelen P, Kutalik Z. Multi-layered genetic approaches to identify approved drug targets. CELL GENOMICS 2023; 3:100341. [PMID: 37492104 PMCID: PMC10363916 DOI: 10.1016/j.xgen.2023.100341] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 07/27/2023]
Abstract
Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Chiara Auwerx
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Patrick Deelen
- Department of Genetics, University of Groningen, 9700 Groningen, the Netherlands
- Oncode Institute, 3521 Utrecht, the Netherlands
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
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193
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Chaka M, Geffe CA, Rodriguez A, Seriani N, Wu Q, Mekonnen YS. High-Throughput Screening of Promising Redox-Active Molecules with MolGAT. ACS OMEGA 2023; 8:24268-24278. [PMID: 37457475 PMCID: PMC10339396 DOI: 10.1021/acsomega.3c01295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023]
Abstract
Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from -4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB, QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as -2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing.
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Affiliation(s)
- Mesfin
Diro Chaka
- Department
of Physics, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia
- Computational
Data Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia
| | - Chernet Amente Geffe
- Department
of Physics, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia
| | - Alex Rodriguez
- The Abdus
Salam International Centre for Theoretical Physics(ICTP) Condensed Matter and Statistical Physics Section, 34100 Trieste, Italy
| | - Nicola Seriani
- The Abdus
Salam International Centre for Theoretical Physics(ICTP) Condensed Matter and Statistical Physics Section, 34100 Trieste, Italy
| | - Qin Wu
- Brookhaven
National Laboratory, Center for Functional Nanomaterials, Upton New York 11973, United States
| | - Yedilfana Setarge Mekonnen
- Center for
Environmental Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa 1176, Ethiopia
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194
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Wang T, Chen M, Li H, Ding G, Song Y, Hou B, Yao B, Wang Z, Hou Y, Liang J, Wei C, Jia Z. Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation. Heliyon 2023; 9:e17603. [PMID: 37449101 PMCID: PMC10336525 DOI: 10.1016/j.heliyon.2023.e17603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 06/17/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Aims To explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology and in vitro experiment. Methods The ingredients library of BZBS was constructed by retrieving multiple TCM databases. The potential target profiles of the components were predicted by target prediction algorithms based on different principles, and validated by using known activity data. The target spectrum of BZBS with high reliability was screened by considering the source of the targets and the node degree in compound-target (C-T) network. Subsequently, new indications for BZBS were predicted by disease ontology (DO) enrichment analysis and initially validated by GO and KEGG pathway enrichment analysis. Furthermore, the target sets of BZBS acting on AD signaling pathway were identified by intersection analysis. Based on STRING database, the PPI network of target was constructed and their node degree was calculated. Two Alzheimer's disease (AD) cell models, BV-2 and SH-SY5Y, were used to preliminarily verify the anti-AD efficacy and mechanism of BZBS in vitro. Results In total, 1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula, and 1320 BZBS targets with high confidence were predicted. Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treatment of AD. GO and KEGG enrichment results provide a preliminary verification of this point. Among them, 113 functional targets of BZBS belong to AD pathway. A PPI network containing 113 functional targets and 1051 edges for the treatment of AD was constructed. In vitro experiments showed that BZBS could significantly reduce the release of TNF-α and IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells, which may be related to the regulation of ERK1/2/NF-κB signaling pathway. BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells, significantly increased mitochondrial membrane potential, reduced the expression of Caspase3 active fragment and PSEN1, and increased the expression of IDE. This may be related to the regulation of GSK-3β/β-catenin signaling pathway. Conclusions BZBS formula has a potential use in the treatment of AD, which is achieved through regulation of ERK1/2, NF-κB signaling pathways, and GSK-3β/β-catenin signaling pathway. Furthermore, the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action. The study lays a foundation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.
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Affiliation(s)
- Tongxing Wang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Meng Chen
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Huixin Li
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Guoyuan Ding
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Yanfei Song
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Bin Hou
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Bing Yao
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Zhixin Wang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Yunlong Hou
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Junqing Liang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Cong Wei
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
- Key Disciplines of State Administration of TCM for Collateral Disease, Shijiazhuang, 050035, PR China
| | - Zhenhua Jia
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
- Key Disciplines of State Administration of TCM for Collateral Disease, Shijiazhuang, 050035, PR China
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195
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Silva JV, Sueyoshi S, Snape TJ, Lal S, Giarolla J. Pteridine reductase (PTR1): initial structure-activity relationships studies of potential leishmanicidal arylindole derivatives compounds. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:661-687. [PMID: 37606690 DOI: 10.1080/1062936x.2023.2247331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/08/2023] [Indexed: 08/23/2023]
Abstract
Leishmaniasis is a public health concern, especially in Brazil and India. The drugs available for therapy are old, cause toxicity and have reports of resistance. Therefore, this paper aimed to carry out initial structure-activity relationships (applying molecular docking and dynamic simulations) of arylindole scaffolds against the pteridine reductase (PTR1), which is essential target for the survival of the parasite. Thus, we used a series of 43 arylindole derivatives as a privileged skeleton, which have been evaluated previously for different biological actions. Compound 7 stood out among its analogues presenting the best results of average number of interactions with binding site (2.00) and catalytic triad (1.00). Additionally, the same compound presented the best binding free energy (-32.33 kcal/mol) in dynamic simulations. Furthermore, with computational studies, it was possible to comprehend and discuss the influences of the substituent sizes, positions of substitutions in the aromatic ring and electronic influences. Therefore, this study can be a starting point for the structural improvements needed to obtain a good leishmanicidal drug.
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Affiliation(s)
- J V Silva
- Department of Pharmacy, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - S Sueyoshi
- Department of Pharmacy, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - T J Snape
- Leicester School of Pharmacy, De Montfort University, Leicester, UK
| | - S Lal
- Amity Institute of Pharmacy, Amity University, Gurugram, India
| | - J Giarolla
- Department of Pharmacy, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
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196
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Arnold A, Alexander J, Liu G, Stokes JM. Applications of machine learning in microbial natural product drug discovery. Expert Opin Drug Discov 2023; 18:1259-1272. [PMID: 37651150 DOI: 10.1080/17460441.2023.2251400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development. AREAS COVERED This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery. EXPERT OPINION Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.
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Affiliation(s)
- Autumn Arnold
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jeremie Alexander
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Gary Liu
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
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197
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Wong AR, Hung A, Yang AWH, Gill H, Lenon GB. Poria cocos compounds targeting neuropeptide Y1 receptor (Y1R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y1R inhibitor. PLoS One 2023; 18:e0277873. [PMID: 37390097 PMCID: PMC10313034 DOI: 10.1371/journal.pone.0277873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 10/09/2022] [Indexed: 07/02/2023] Open
Abstract
Poria cocos (PC) is a medicinal herb frequently used in weight-loss clinical trials, however the mechanisms by which its compounds target orexigenic receptors including the neuropeptide Y1 receptor (Y1R) remain largely unknown. This study aimed to screen PC compounds for favourable pharmacokinetics profiles and examine their molecular mechanisms targeting Y1R. Forty-three PC compounds were systematically sought from pharmacological databases and docked with Y1R (PDB: 5ZBQ). By comparing the relative binding affinities, pharmacokinetics and toxicity profiles, we hypothesised that compounds designated PC1 3,4-Dihydroxybenzoic acid, PC8 Vanillic acid, PC40 1-(alpha-L-Ribofuranosyl)uracil, could be potential antagonists as they contact major residues Asn283 and Asp287, similar to various potent Y1R antagonists. In addition, PC21 Poricoic acid B, PC22 Poricoic acid G and PC43 16alpha,25-Dihydroxy-24-methylene-3,4-secolanosta-4(28),7,9(11)-triene-3,21-dioic acid, contacting Asn299, Asp104 and Asp200 proximal to the extracellular surface could also interfere with agonist binding by stabilising the extracellular loop (ECL) 2 of Y1R in a closed position. Owing to their selective interaction with Phe302, an important residue in binding of selective Y1R antagonists, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were proposed as putative antagonists. Following the consensus approach, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were identified as candidate compounds due to their high affinities (-12.2, -11.0 and -10.8 kcal, respectively), high drug-likeness and low toxicity profiles. Trajectory analyses and energy contributions of PC12-Y1R complex further confirmed their structural stability and favourable binding free energies, highlighting the feasibility and possible development of PC12 beta-Amyrin acetate as a future Y1R inhibitor.
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Affiliation(s)
- Ann Rann Wong
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
| | - Andrew Hung
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Angela Wei Hong Yang
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
| | - Harsharn Gill
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - George Binh Lenon
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
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198
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张 立, 秦 玉, 陈 明. [SMILESynergy: Anticancer drug synergy prediction based on Transformer pre-trained model]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:544-551. [PMID: 37380395 PMCID: PMC10307610 DOI: 10.7507/1001-5515.202209043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 05/05/2023] [Indexed: 06/30/2023]
Abstract
The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.
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Affiliation(s)
- 立强 张
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
| | - 玉芳 秦
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
| | - 明 陈
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
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199
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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200
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Varikoti RA, Schultz KJ, Kombala CJ, Kruel A, Brandvold KR, Zhou M, Kumar N. Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease. J Comput Aided Mol Des 2023:10.1007/s10822-023-00509-1. [PMID: 37314632 DOI: 10.1007/s10822-023-00509-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/23/2023] [Indexed: 06/15/2023]
Abstract
Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.
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Affiliation(s)
- Rohith Anand Varikoti
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Katherine J Schultz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Chathuri J Kombala
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Agustin Kruel
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Kristoffer R Brandvold
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Mowei Zhou
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Neeraj Kumar
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA.
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