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Boonyarit B, Yamprasert N, Kaewnuratchadasorn P, Kinchagawat J, Prommin C, Rungrotmongkol T, Nutanong S. GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity. J Comput Chem 2024; 45:2001-2023. [PMID: 38713612 DOI: 10.1002/jcc.27388] [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: 01/08/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/09/2024]
Abstract
The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.
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Affiliation(s)
- Bundit Boonyarit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Nattawin Yamprasert
- School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | | | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Chanatkran Prommin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
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2
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Mariani R, De Vuono MC, Businaro E, Ivaldi S, Dell'Armi T, Gallo M, Ardigò D. P.O.L.A.R. Star: A New Framework Developed and Applied by One Mid-Sized Pharmaceutical Company to Drive Digital Transformation in R&D. Pharmaceut Med 2024:10.1007/s40290-024-00533-y. [PMID: 39120788 DOI: 10.1007/s40290-024-00533-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/25/2024] [Indexed: 08/10/2024]
Abstract
Digital transformation has become a cornerstone of innovation in pharmaceutical research and development (R&D). Pharmaceutical companies now have an imperative to embrace transformation, including mid-sized and small-sized companies despite resource limitations that do not allow economies of scale compared with larger organizations. This article describes the journey undertaken by Chiesi to develop an efficient framework to drive digital transformation along its R&D value chain with the objective of building and refreshing a clear roadmap and relevant priorities, together with identifying and enabling new digital capabilities and skills within R&D, defining tools and processes that will guide Chiesi activities in the space up to mid-long term. This work has led so far to five main achievements, which align with the steps in the framework: a strategically aligned roadmap with key focus areas for digital transformation and a dedicated team to lead the effort; a common language for data across the R&D value chain; an internal mindset that's open to innovation and participation in key external networks and consortia; a set of quick-win use cases for the new framework; and a defined set of Key Performance Indicators (KPIs) and monitoring tools for digital transformation. The work presented here demonstrates that R&D digital transformation should represent an ongoing process to enable cross-functional collaboration and integration within complex corporate environments that face an ever-growing volume of diverse data, to efficiently support business needs, and to ensure a positive impact on patient care.
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Affiliation(s)
- Riccardo Mariani
- Chiesi Farmaceutici Spa, Largo Francesco Belloli 11/A, 43122, Parma, Italy.
| | | | - Elena Businaro
- Chiesi Farmaceutici Spa, Largo Francesco Belloli 11/A, 43122, Parma, Italy
| | - Silvia Ivaldi
- Chiesi Farmaceutici Spa, Largo Francesco Belloli 11/A, 43122, Parma, Italy
| | | | | | - Diego Ardigò
- Chiesi Farmaceutici Spa, Largo Francesco Belloli 11/A, 43122, Parma, Italy
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Li G, Li S, Liang C, Xiao Q, Luo J. Drug repositioning based on residual attention network and free multiscale adversarial training. BMC Bioinformatics 2024; 25:261. [PMID: 39118000 PMCID: PMC11308596 DOI: 10.1186/s12859-024-05893-5] [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: 06/30/2023] [Accepted: 08/06/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. RESULTS This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. CONCLUSIONS The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China.
| | - Shuwen Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
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Liu Y, Xu C, Yang X, Zhang Y, Chen Y, Liu H. Application progress of deep generative models in de novo drug design. Mol Divers 2024:10.1007/s11030-024-10942-5. [PMID: 39097862 DOI: 10.1007/s11030-024-10942-5] [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: 05/24/2024] [Accepted: 07/16/2024] [Indexed: 08/05/2024]
Abstract
The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.
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Affiliation(s)
- Yingxu Liu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Chengcheng Xu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Xinyi Yang
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Yanmin Zhang
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Yadong Chen
- School of Science, China Pharmaceutical University, Nanjing, 210009, China
| | - Haichun Liu
- School of Science, China Pharmaceutical University, Nanjing, 210009, China.
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Duo L, Liu Y, Ren J, Tang B, Hirst JD. Artificial intelligence for small molecule anticancer drug discovery. Expert Opin Drug Discov 2024; 19:933-948. [PMID: 39074493 DOI: 10.1080/17460441.2024.2367014] [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: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.
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Affiliation(s)
- Lihui Duo
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Yu Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jianfeng Ren
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Bencan Tang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park, Nottingham, UK
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Xu C, Zheng L, Fan Q, Liu Y, Zeng C, Ning X, Liu H, Du K, Lu T, Chen Y, Zhang Y. Progress in the application of artificial intelligence in molecular generation models based on protein structure. Eur J Med Chem 2024; 277:116735. [PMID: 39098131 DOI: 10.1016/j.ejmech.2024.116735] [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: 05/20/2024] [Revised: 07/12/2024] [Accepted: 07/30/2024] [Indexed: 08/06/2024]
Abstract
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyzing a series of novel molecular generation models predicated on protein structures. Initially, we categorize the molecular generation models based on protein structures and highlight the architectural frameworks utilized in these models. Subsequently, we detail the design and implementation of protein structure-based molecular generation models by introducing different specific examples. Lastly, we outline the current opportunities and challenges encountered in this field, intending to offer guidance and a referential framework for developing and studying new models in related fields in the future.
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Affiliation(s)
- Chengcheng Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Lidan Zheng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Qing Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Yingxu Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Chen Zeng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Xiangzhen Ning
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Ke Du
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China; State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
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Zhao Y, Wan Q, He X. Construction of IRAK4 inhibitor activity prediction model based on machine learning. Mol Divers 2024:10.1007/s11030-024-10926-5. [PMID: 38970641 DOI: 10.1007/s11030-024-10926-5] [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: 05/22/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024]
Abstract
Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R2 of 0.829, an MAE of 0.317, and an RMSE of 0.460 in independent testing. To further validate the model's generalization ability, we tested it on 90 IRAK4 inhibitors collected in 2023. Subsequently, we applied the model to predict the activity of 13,268 compounds with docking scores less than - 9.503 kcal/mol. These compounds were initially screened from a pool of 1.6 million molecules in the chemdiv database through high-throughput molecular docking. Among these, 259 compounds with predicted pIC50 values greater than or equal to 8.00 were identified. We then performed ADMET predictions on these selected compounds. Finally, through a rigorous screening process, we identified 34 compounds that adhere to the four complementary drug-likeness rules, making them promising candidates for further investigation. Additionally, molecular dynamics simulations confirmed the stable binding of the screened compounds to the IRAK4 protein. Overall, this work presents a machine learning model for accurate prediction of IRAK4 inhibitor activity and offers new insights for subsequent structure-guided design of novel IRAK4 inhibitors.
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Affiliation(s)
- Yihuan Zhao
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China.
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China.
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China.
| | - Qianwen Wan
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China
| | - Xiaoyu He
- Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi, 563006, People's Republic of China
- Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education, Zunyi Medical University, Zunyi, 563006, China
- The Key Laboratory of Clinical Pharmacy of Zunyi City, Zunyi Medical University, Zunyi, 563006, China
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Gangwal A, Ansari A, Ahmad I, Azad AK, Wan Sulaiman WMA. Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review. Comput Biol Med 2024; 179:108734. [PMID: 38964243 DOI: 10.1016/j.compbiomed.2024.108734] [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: 03/07/2024] [Revised: 06/01/2024] [Accepted: 06/08/2024] [Indexed: 07/06/2024]
Abstract
Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule, 424001, Maharashtra, India.
| | - Azim Ansari
- Computer Aided Drug Design Center, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule, 424001, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Gondur, Dhule, 424002, Maharashtra, India.
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, 68100, Kuala Lumpur, Malaysia.
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Vittoria Togo M, Mastrolorito F, Orfino A, Graps EA, Tondo AR, Altomare CD, Ciriaco F, Trisciuzzi D, Nicolotti O, Amoroso N. Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives. Expert Opin Drug Metab Toxicol 2024; 20:561-577. [PMID: 38141160 DOI: 10.1080/17425255.2023.2298827] [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: 09/05/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.
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Affiliation(s)
- Maria Vittoria Togo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fabrizio Mastrolorito
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Angelica Orfino
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Elisabetta Anna Graps
- ARESS Puglia - Agenzia Regionale strategica per laSalute ed il Sociale, Presidenza della Regione Puglia", Bari, Italy
| | - Anna Rita Tondo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Cosimo Damiano Altomare
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fulvio Ciriaco
- Department of Chemistry, Universitá degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Daniela Trisciuzzi
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Orazio Nicolotti
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Nicola Amoroso
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
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Seixas Feio JA, de Oliveira ECL, de Sales CDS, da Costa KS, e Lima AHL. Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale. PLoS One 2024; 19:e0305253. [PMID: 38870192 PMCID: PMC11175476 DOI: 10.1371/journal.pone.0305253] [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: 12/11/2023] [Accepted: 05/27/2024] [Indexed: 06/15/2024] Open
Abstract
Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.
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Affiliation(s)
- Juliana Auzier Seixas Feio
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campus Belém, Instituto de Tecnologia, Universidade Federal do Pará, Pará, Brazil
| | - Ewerton Cristhian Lima de Oliveira
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campus Belém, Instituto de Tecnologia, Universidade Federal do Pará, Pará, Brazil
- Instituto Tecnológico Vale, Belém, Pará, Brazil
| | - Claudomiro de Souza de Sales
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campus Belém, Instituto de Tecnologia, Universidade Federal do Pará, Pará, Brazil
| | - Kauê Santana da Costa
- Laboratório de Simulação Computacional, Campus Marechal Rondom, Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
| | - Anderson Henrique Lima e Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Pará, Brazil
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11
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Chen X, Huang J, Shen T, Zhang H, Xu L, Yang M, Xie X, Yan Y, Yan J. DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention. Bioinformatics 2024; 40:btae319. [PMID: 38897656 PMCID: PMC11193059 DOI: 10.1093/bioinformatics/btae319] [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: 12/15/2023] [Revised: 03/23/2024] [Accepted: 06/17/2024] [Indexed: 06/21/2024] Open
Abstract
MOTIVATION Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges. RESULTS We propose an innovative neural network model called DEAttentionDTA, based on dynamic word embeddings and a self-attention mechanism, for predicting protein-ligand binding affinity. DEAttentionDTA takes the 1D sequence information of proteins as input, including the global sequence features of amino acids, local features of the active pocket site, and linear representation information of the ligand molecule in the SMILE format. These three linear sequences are fed into a dynamic word-embedding layer based on a 1D convolutional neural network for embedding encoding and are correlated through a self-attention mechanism. The output affinity prediction values are generated using a linear layer. We compared DEAttentionDTA with various mainstream tools and achieved significantly superior results on the same dataset. We then assessed the performance of this model in the p38 protein family. AVAILABILITY AND IMPLEMENTATION The resource codes are available at https://github.com/whatamazing1/DEAttentionDTA.
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Affiliation(s)
- Xiying Chen
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jinsha Huang
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tianqiao Shen
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Houjin Zhang
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Li Xu
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Min Yang
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiaoman Xie
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yunjun Yan
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jinyong Yan
- Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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12
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Barone F, Russo ET, Villegas Garcia EN, Punta M, Cozzini S, Ansuini A, Cazzaniga A. Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering. Sci Data 2024; 11:568. [PMID: 38824125 PMCID: PMC11144186 DOI: 10.1038/s41597-024-03131-4] [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: 06/13/2023] [Accepted: 03/08/2024] [Indexed: 06/03/2024] Open
Abstract
Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository.
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Affiliation(s)
- Federico Barone
- Area Science Park, Padriciano, 99, 34149, Trieste, Italy
- University of Trieste, Trieste, 34127, Italy
| | | | | | - Marco Punta
- IRCCS San Raffaele Institute, Center for Omics Sciences, Milan, 20132, Italy
- IRCCS San Raffaele Institute, Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, Milan, 20132, Italy
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13
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Jardim C, de Waal A, Fabris-Rotelli I, Rad NN, Mazarura J, Sherry D. Feature engineered embeddings for classification of molecular data. Comput Biol Chem 2024; 110:108056. [PMID: 38796282 DOI: 10.1016/j.compbiolchem.2024.108056] [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: 11/17/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 05/28/2024]
Abstract
The classification of molecules is of particular importance to the drug discovery process and several other use cases. Data in this domain can be partitioned into structural and sequence/text data. Several techniques such as deep learning are able to classify molecules and predict their functions using both types of data. Molecular structure and encoded chemical information are sufficient to classify a characteristic of a molecule. However, the use of a molecule's structural information typically requires large amounts of computational power with deep learning models that take a long time to train. In this study, we present an alternative approach to molecule classification that addresses the limitations of other techniques. This approach uses natural language processing techniques in the form of count vectorisation, term frequency-inverse document frequency, word2vec and Latent Dirichlet Allocation to feature engineer molecular text data. Through this approach, we aim to make a robust and easily reproducible embedding that is fast to implement and solely dependent on chemical (text) data such as the sequence of a protein. Further, we investigate the usefulness of these embeddings for machine learning models. We apply the techniques to two different types of molecular text data: FASTA sequence data and Simplified Molecular Input Line Entry Specification data. We show that these embeddings provide excellent performance for classification.
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14
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Kadsanit N, Worsawat P, Sakonsinsiri C, McElroy CR, Macquarrie D, Noppawan P, Hunt AJ. Sustainable methods for the carboxymethylation and methylation of ursolic acid with dimethyl carbonate under mild and acidic conditions. RSC Adv 2024; 14:16921-16934. [PMID: 38799212 PMCID: PMC11124730 DOI: 10.1039/d4ra02122c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Ursolic acid is a triterpene plant extract that exhibits significant potential as an anti-cancer, anti-tumour, and anti-inflammatory agent. Its direct use in the pharmaceutical industry is hampered by poor uptake of ursolic acid in the human body coupled with rapid metabolism causing a decrease in bioactivity. Modification of ursolic acid can overcome such issues, however, use of toxic reagents, unsustainable synthetic routes and poor reaction metrics have limited its potential. Herein, we demonstrate the first reported carboxymethylation and/or methylation of ursolic acid with dimethyl carbonate (DMC) as a green solvent and sustainable reagent under acidic conditions. The reaction of DMC with ursolic acid, in the presence of PTSA, ZnCl2, or H2SO4-SiO2 yielded the carboxymethylation product 3β-[[methoxy]carbonyl]oxyurs-12-en-28-oic acid, the methylation product 3β-methoxyurs-12-en-28-oic acid and the dehydration product urs-2,12-dien-28-oic acid. PTSA demonstrated high conversion and selectivity towards the previously unreported carboxymethylation of ursolic acid, while the application of formic acid in the system led to formylation of ursolic acid (3β-formylurs-12-en-28-oic acid) in quantitative yields via esterification, with DMC acting solely as a solvent. Meanwhile, the methylation product of ursolic acid, 3β-methoxyurs-12-en-28-oic acid, was successfully synthesised with FeCl3, demonstrating exceptional conversion and selectivity, >99% and 99%, respectively. Confirmed with the use of qualitative and quantitative green metrics, this result represents a significant improvement in conversion, selectivity, safety, and sustainability over previously reported methods of ursolic acid modification. It was demonstrated that these methods could be applied to other triterpenoids, including corosolic acid. The study also explored the potential pharmaceutical applications of ursolic acid, corosolic acid, and their derivatives, particularly in anti-inflammatory, anti-cancer, and anti-tumour treatments, using molecular ADMET and docking methods. The methods developed in this work have led to the synthesis of novel molecules, thus creating opportunities for the future investigation of biological activity and the modification of a wide range of triterpenoids applying acidic DMC systems to deliver novel active pharmaceutical intermediates.
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Affiliation(s)
- Nuttapong Kadsanit
- Materials Chemistry Research Center (MCRC), Department of Chemistry and Centre of Excellence for Innovation in Chemistry, Faculty of Science, Khon Kaen University Khon Kaen 40002 Thailand
| | - Pattamabhorn Worsawat
- Materials Chemistry Research Center (MCRC), Department of Chemistry and Centre of Excellence for Innovation in Chemistry, Faculty of Science, Khon Kaen University Khon Kaen 40002 Thailand
| | - Chadamas Sakonsinsiri
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University Khon Kaen 40002 Thailand
| | - Con R McElroy
- School of Chemistry, University of Lincoln Brayford Pool Campus Lincoln LN6 7TS UK
- Green Chemistry Centre of Excellence, Department of Chemistry, University of York Heslington York YO10 5DD UK
| | - Duncan Macquarrie
- Green Chemistry Centre of Excellence, Department of Chemistry, University of York Heslington York YO10 5DD UK
| | - Pakin Noppawan
- Department of Chemistry, Faculty of Science, Mahasarakham University Maha Sarakham 44150 Thailand
| | - Andrew J Hunt
- Materials Chemistry Research Center (MCRC), Department of Chemistry and Centre of Excellence for Innovation in Chemistry, Faculty of Science, Khon Kaen University Khon Kaen 40002 Thailand
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15
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Yucel MA, Adal E, Aktekin MB, Hepokur C, Gambacorta N, Nicolotti O, Algul O. From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors. ChemMedChem 2024:e202400108. [PMID: 38726553 DOI: 10.1002/cmdc.202400108] [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: 02/05/2024] [Revised: 04/24/2024] [Indexed: 07/21/2024]
Abstract
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.
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Affiliation(s)
- Mehmet Ali Yucel
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
| | - Ercan Adal
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Mine Buga Aktekin
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
| | - Ceylan Hepokur
- Department of Biochemistry, Faculty of Pharmacy, Sivas Cumhuriyet University, 58140, Sivas, Türkiye
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Universita 'degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, Bari I, 70125, Italy
| | - Oztekin Algul
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Erzincan Binali Yildirim University, 24002, Erzincan, Türkiye
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mersin University, 33160, Mersin, Türkiye
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16
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Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [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: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.
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Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
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17
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Duo L, Chen Y, Liu Q, Ma Z, Farjudian A, Ho WY, Low SS, Ren J, Hirst JD, Xie H, Tang B. Discovery of novel SOS1 inhibitors using machine learning. RSC Med Chem 2024; 15:1392-1403. [PMID: 38665844 PMCID: PMC11042245 DOI: 10.1039/d4md00063c] [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: 01/23/2024] [Accepted: 03/14/2024] [Indexed: 04/28/2024] Open
Abstract
Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy for RAS-driven cancers. Applying machine learning (ML)-based virtual screening (VS) on small-molecule databases, we selected a random forest (RF) regressor for its robustness and performance. Screening was performed with the L-series and EGFR-related datasets, and was extended to the Chinese National Compound Library (CNCL) with more than 1.4 million compounds. In addition to a series of documented SOS1-related molecules, we uncovered nine compounds that have an unexplored chemical framework and displayed inhibitory activity, with the most potent achieving more than 50% inhibition rate in the KRAS G12C/SOS1 PPI assay and an IC50 value in the proximity of 20 μg mL-1. Compared with the manner that known inhibitory agents bind to the target, hit compounds represented by CL01545365 occupy a unique pocket in molecular docking. An in silico drug-likeness assessment suggested that the compound has moderately favorable drug-like properties and pharmacokinetic characteristics. Altogether, our findings strongly support that, characterized by the distinctive binding modes, the recognition of novel skeletons from the carboxylic acid series could be candidates for developing promising SOS1 inhibitors.
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Affiliation(s)
- Lihui Duo
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Yi Chen
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
- University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China
| | - Qiupei Liu
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
| | - Zhangyi Ma
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Amin Farjudian
- School of Mathematics, Watson Building, University of Birmingham Edgbaston Birmingham B15 2TT UK
| | - Wan Yong Ho
- Faculty of Medicine and Health Sciences, University of Nottingham (Malaysia Campus) Semenyih 43500 Malaysia
| | - Sze Shin Low
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Jianfeng Ren
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park Nottingham NG7 2RD UK
| | - Hua Xie
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road 201203 Shanghai China
- University of Chinese Academy of Sciences No.19A Yuquan Road Beijing 100049 China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences Zhongshan Tsuihang New District Zhongshan 528400 China
| | - Bencan Tang
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China
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18
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Gu Z, Yan Y, Liu H, Wu D, Yao H, Lin K, Li X. Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network. J Chem Inf Model 2024; 64:3047-3058. [PMID: 38520328 DOI: 10.1021/acs.jcim.3c01900] [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: 03/25/2024]
Abstract
Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).
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Affiliation(s)
- Zhihao Gu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yong Yan
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hanwen Liu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Di Wu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Xuanyi Li
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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19
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Gao C, Bao W, Wang S, Zheng J, Wang L, Ren Y, Jiao L, Wang J, Wang X. DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation. Brief Funct Genomics 2024:elae011. [PMID: 38582610 DOI: 10.1093/bfgp/elae011] [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: 11/09/2023] [Revised: 02/25/2024] [Accepted: 03/13/2024] [Indexed: 04/08/2024] Open
Abstract
Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.
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Affiliation(s)
- Changnan Gao
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Wenjie Bao
- Guanghua School of Management, Peking University, Beijing 100091, China
| | - Shuang Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Jianyang Zheng
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Lulu Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Yongqi Ren
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Linfang Jiao
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea
| | - Xun Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
- High Performance Computer Research Center, Institute of Computing Technology, CAS, Beijing 100190, China
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20
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Corbaux P, Bayle A, Besle S, Vinceneux A, Vanacker H, Ouali K, Hanvic B, Baldini C, Cassier PA, Terret C, Verlingue L. Patients' selection and trial matching in early-phase oncology clinical trials. Crit Rev Oncol Hematol 2024; 196:104307. [PMID: 38401694 DOI: 10.1016/j.critrevonc.2024.104307] [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: 11/15/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications. DESIGN We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation. RESULTS New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates. CONCLUSIONS Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.
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Affiliation(s)
- P Corbaux
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Medical Oncology, Institut de Cancérologie et d'Hématologie Universitaire de Saint-Étienne (ICHUSE), Centre Hospitalier Universitaire de Saint-Etienne, France
| | - A Bayle
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - S Besle
- Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - A Vinceneux
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - H Vanacker
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - K Ouali
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - B Hanvic
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - C Baldini
- Drug Development Department (DITEP), Gustave Roussy, Université Paris-Saclay, Villejuif F-94805, France
| | - P A Cassier
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France
| | - C Terret
- Medical Oncology Department, Centre Léon Bérard, Lyon, France
| | - L Verlingue
- Medical Oncology Department, Centre Léon Bérard, Lyon, France; Centre de Recherche en Cancérologie de Lyon (CRCL), France.
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21
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Li X, Yang Q, Xu L, Dong W, Luo G, Wang W, Dong S, Wang K, Xuan P, Zhang X, Gao X. DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins. Bioinformatics 2024; 40:btae176. [PMID: 38561176 PMCID: PMC11015954 DOI: 10.1093/bioinformatics/btae176] [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: 08/19/2023] [Revised: 03/04/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
MOTIVATION Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION https://github.com/lixiaokun2020/DrugMGR.
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Affiliation(s)
- Xiaokun Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Qiang Yang
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150000, China
| | - Long Xu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Weihe Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, KAUST, Thuwal 23955, Saudi Arabia
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Xianyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, KAUST, Thuwal 23955, Saudi Arabia
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22
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Zhang C, Xie L, Lu X, Mao R, Xu L, Xu X. Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery. Molecules 2024; 29:1499. [PMID: 38611779 PMCID: PMC11013495 DOI: 10.3390/molecules29071499] [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: 01/25/2024] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.
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Affiliation(s)
| | | | | | | | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (C.Z.); (L.X.); (X.L.); (R.M.)
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China; (C.Z.); (L.X.); (X.L.); (R.M.)
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23
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Katsoulakis E, Wang Q, Wu H, Shahriyari L, Fletcher R, Liu J, Achenie L, Liu H, Jackson P, Xiao Y, Syeda-Mahmood T, Tuli R, Deng J. Digital twins for health: a scoping review. NPJ Digit Med 2024; 7:77. [PMID: 38519626 PMCID: PMC10960047 DOI: 10.1038/s41746-024-01073-0] [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: 08/22/2023] [Accepted: 03/07/2024] [Indexed: 03/25/2024] Open
Abstract
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
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Affiliation(s)
- Evangelia Katsoulakis
- VA Informatics and Computing Infrastructure, Salt Lake City, UT, 84148, USA
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Qi Wang
- Department of Mathematics, University of South Carolina, Columbia, SC, 29208, USA
| | - Huanmei Wu
- Department of Health Services Administration and Policy, Temple University, Philadelphia, PA, 19122, USA
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Richard Fletcher
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02139, USA
| | - Jinwei Liu
- Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Luke Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hongfang Liu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pamela Jackson
- Precision Neurotherapeutics Innovation Program & Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85003, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Richard Tuli
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, 06510, USA.
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24
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Siddique F, Anwaar A, Bashir M, Nadeem S, Rawat R, Eyupoglu V, Afzal S, Bibi M, Bin Jardan YA, Bourhia M. Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach. Front Chem 2024; 12:1380266. [PMID: 38576849 PMCID: PMC10991842 DOI: 10.3389/fchem.2024.1380266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/05/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.
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Affiliation(s)
- Farhan Siddique
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Ahmar Anwaar
- Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Maryam Bashir
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
- Southern Punjab Institute of Health Sciences, Multan, Pakistan
| | - Sumaira Nadeem
- Department of Pharmacy, The Women University, Multan, Pakistan
| | - Ravi Rawat
- School of Health Sciences & Technology, UPES University, Dehradun, India
| | - Volkan Eyupoglu
- Department of Chemistry, Cankırı Karatekin University, Cankırı, Türkiye
| | - Samina Afzal
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Mehvish Bibi
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Yousef A. Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
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25
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Shoombuatong W, Homdee N, Schaduangrat N, Chumnanpuen P. Leveraging a meta-learning approach to advance the accuracy of Na v blocking peptides prediction. Sci Rep 2024; 14:4463. [PMID: 38396246 PMCID: PMC10891130 DOI: 10.1038/s41598-024-55160-z] [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: 12/28/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
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Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand
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26
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Malusare A, Aggarwal V. Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model. ARXIV 2024:arXiv:2402.08790v1. [PMID: 38410649 PMCID: PMC10896363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance generative processes has not been realized. In this paper, we present a novel approach that bridges this divide by developing a framework for knowledge-enhanced generative models called K-DReAM. We develop a scalable methodology to extend the functionality of knowledge graphs while preserving semantic integrity, and incorporate this contextual information into a generative framework to guide a diffusion-based model. The integration of knowledge graph embeddings with our generative model furnishes a robust mechanism for producing novel drug candidates possessing specific characteristics while ensuring validity and synthesizability. K-DReAM outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.
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Affiliation(s)
- Aditya Malusare
- School of Industrial Engineering, Purdue University, USA
- Purdue Institute for Cancer Research, Purdue University, USA
| | - Vaneet Aggarwal
- School of Industrial Engineering, Purdue University, USA
- Purdue Institute for Cancer Research, Purdue University, USA
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27
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Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel) 2024; 14:233. [PMID: 38398742 PMCID: PMC10890405 DOI: 10.3390/life14020233] [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: 01/09/2024] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.
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Affiliation(s)
| | - Irina Negut
- National Institute for Lasers, Plasma and Radiation Physics, 409 Atomistilor Street, 077125 Magurele, Ilfov, Romania;
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28
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Cichońska A, Ravikumar B, Rahman R. AI for targeted polypharmacology: The next frontier in drug discovery. Curr Opin Struct Biol 2024; 84:102771. [PMID: 38215530 DOI: 10.1016/j.sbi.2023.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024]
Abstract
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.
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29
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Akermi S, Smaoui S, Chaari M, Elhadef K, Gentile R, Hait M, Roymahapatra G, Mellouli L. Combined in vitro/in silico approaches, molecular dynamics simulations and safety assessment of the multifunctional properties of thymol and carvacrol: A comparative insight. Chem Biodivers 2024; 21:e202301575. [PMID: 38116885 DOI: 10.1002/cbdv.202301575] [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: 10/07/2023] [Revised: 12/11/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023]
Abstract
Bioactive compounds derived from medicinal plants have acquired immense attentiveness in drug discovery and development. The present study investigated in vitro and predicted in silico the antibacterial, antifungal, and antiviral properties of thymol and carvacrol, and assessed their safety. The performed microbiological assays against Pseudomonas aeruginosa, Escherichia coli, Salmonella enterica Typhimurium revealed that the minimal inhibitory concentration values ranged from (0.078 to 0.312 mg/mL) and the minimal fungicidal concentration against Candida albicans was 0.625 mg/mL. Molecular docking simulations, stipulated that these compounds could inhibit bacterial replication and transcription functions by targeting DNA and RNA polymerases receptors with docking scores varying between (-5.1 to -6.9 kcal/mol). Studied hydroxylated monoterpenes could hinder C. albicans growth by impeding lanosterol 14α-demethylase enzyme and showed a (ΔG=-6.2 and -6.3 kcal/mol). Computational studies revealed that thymol and carvacrol could target the SARS-Cov-2 spike protein of the Omicron variant RBD domain. Molecular dynamics simulations disclosed that these compounds have a stable dynamic behavior over 100 ns as compared to remdesivir. Chemo-computational toxicity prediction using Protox II webserver indicated that thymol and carvacrol could be safely and effectively used as drug candidates to tackle bacterial, fungal, and viral infections as compared to chemical medication.
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Affiliation(s)
- Sarra Akermi
- Laboratory of Microbial and Enzymatic Biotechnologies and Biomolecules. Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax, 3018, Sfax-, Tunisia
| | - Slim Smaoui
- Laboratory of Microbial and Enzymatic Biotechnologies and Biomolecules. Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax, 3018, Sfax-, Tunisia
| | - Moufida Chaari
- Laboratory of Microbial and Enzymatic Biotechnologies and Biomolecules. Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax, 3018, Sfax-, Tunisia
| | - Khaoula Elhadef
- Laboratory of Microbial and Enzymatic Biotechnologies and Biomolecules. Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax, 3018, Sfax-, Tunisia
| | - Rocco Gentile
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Milan Hait
- Department of Chemistry, Dr. C. V. Raman University, Kota, 495113, Bilaspur, India
| | | | - Lotfi Mellouli
- Laboratory of Microbial and Enzymatic Biotechnologies and Biomolecules. Center of Biotechnology of Sfax (CBS), University of Sfax, Road of Sidi Mansour Km 6, P.O. Box 1177, Sfax, 3018, Sfax-, Tunisia
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30
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Dakilah I, Harb A, Abu-Gharbieh E, El-Huneidi W, Taneera J, Hamoudi R, Semreen MH, Bustanji Y. Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine. Front Pharmacol 2024; 15:1324001. [PMID: 38313315 PMCID: PMC10834672 DOI: 10.3389/fphar.2024.1324001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024] Open
Abstract
The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.
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Affiliation(s)
- Ibraheem Dakilah
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Amani Harb
- Department of Basic Sciences, Faculty of Arts and Sciences, Al-Ahliyya Amman University, Amman, Jordan
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Jalal Taneera
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Mohammed H Semreen
- College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
| | - Yasser Bustanji
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman, Jordan
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31
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T FX, R S, A K FR, S B, R K, M A, S V, S P, S A, K S, M T. Phytochemical composition, anti-microbial, anti-oxidant and anti-diabetic effects of Solanum elaeagnifolium Cav. leaves: in vitro and in silico assessments. J Biomol Struct Dyn 2024:1-27. [PMID: 38180058 DOI: 10.1080/07391102.2023.2300124] [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: 06/09/2023] [Accepted: 12/20/2023] [Indexed: 01/06/2024]
Abstract
The aim of this study was to screen the chemical components of Solanum elaeagnifolium leaves and assess their therapeutic attributes with regard to their antioxidant, antibacterial, and antidiabetic activities. The antidiabetic effects were explored to determine the α-amylase and α-glucosidase inhibitory potential of the leaf extract. To identify the active antidiabetic drugs from the extracts, the GC-MS-screened molecules were docked with diabetes-related proteins using the glide module in the Schrodinger Tool. In addition, molecular dynamics (MD) simulations were performed for 100 ns to evaluate the binding stability of the docked complex using the Desmond module. The ethyl acetate had a significant total phenolic content (TPC), with a value of 79.04 ± 0.98 mg/g GAE. The ethanol extract was tested for its minimum inhibitory concentration (MIC) for its bacteriostatic properties. It suppressed the growth of B. subtilis, E. coli, P. vulgaris, R. equi and S. epidermis at a dosage of 118.75 µg/mL. Moreover, the IC50 values of the ethanol extract were determined to be 17.78 ± 2.38 in the α-amylase and and 27.90 ± 5.02 µg/mL in α-glucosidase. The in-silico investigation revealed that cyclolaudenol achieved docking scores of -7.94 kcal/mol for α-amylase. Likewise, the α-tocopherol achieved the docking scores of -7.41 kcal/mol for glycogen phosphorylase B and -7.21 kcal/mol for phosphorylase kinase. In the MD simulations, the cyclolaudenol and α-tocopherol complexes exhibited consistently stable affinities with diabetic proteins throughout the trajectory. Based on these findings, we conclude that this plant could be a good source for the development of novel antioxidant, antibacterial, and antidiabetic agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Francis Xavier T
- Ethnopharmacological Research Unit, PG and Research Department of Botany, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
| | - Sabitha R
- Ethnopharmacological Research Unit, PG and Research Department of Botany, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
| | - Freeda Rose A K
- PG and Research Department of Botany, Holy Cross College (Autonomous), Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
| | - Balavivekananthan S
- Ethnopharmacological Research Unit, PG and Research Department of Botany, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
| | - Kariyat R
- Department of Biology, The University of Texas, Rio Grande Valley, W University Dr, Edinburg, TX, USA
| | - Ayyanar M
- PG and Research Department of Botany, A.V.V.M. Sri Pushpam College (Autonomous), Bharathidasan University, Poondi, Tamil Nadu, India
| | - Vijayakumar S
- PG and Research Department of Botany, A.V.V.M. Sri Pushpam College (Autonomous), Bharathidasan University, Poondi, Tamil Nadu, India
| | - Prabhu S
- Division of Phytochemistry and Drug Design, Department of Biosciences, Rajagiri College of Social Sciences, Cochin, Kerala, India
| | - Amalraj S
- Division of Phytochemistry and Drug Design, Department of Biosciences, Rajagiri College of Social Sciences, Cochin, Kerala, India
| | - Shine K
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Thiruvengadam M
- Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul, Korea
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Nazli A, Qiu J, Tang Z, He Y. Recent Advances and Techniques for Identifying Novel Antibacterial Targets. Curr Med Chem 2024; 31:464-501. [PMID: 36734893 DOI: 10.2174/0929867330666230123143458] [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: 05/24/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly. METHODS In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification. RESULTS Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well. CONCLUSION The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.
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Affiliation(s)
- Adila Nazli
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
| | - Jingyi Qiu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Ziyi Tang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Yun He
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
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Provasi D, Filizola M. Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques. J Phys Chem B 2023; 127:10691-10699. [PMID: 38084046 PMCID: PMC11252170 DOI: 10.1021/acs.jpcb.3c05306] [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] [Indexed: 12/22/2023]
Abstract
The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, using these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning in building robust deep learning models to enhance ligand bioactivity prediction for each individual opioid receptor (OR) subtype. This is achieved by leveraging knowledge obtained from pretraining a model using supervised learning on a larger data set of bioactivity data combined with ligand-based and structure-based molecular descriptors related to the entire OR subfamily. Our studies hold the potential to advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.
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Affiliation(s)
- Davide Provasi
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, New York 10029, United States
| | - Marta Filizola
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, New York 10029, United States
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34
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Ali S, Shaikh S, Ahmad K, Choi I. Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations. J Biomol Struct Dyn 2023:1-10. [PMID: 38100571 DOI: 10.1080/07391102.2023.2292299] [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: 07/25/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023]
Abstract
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse side effects of existing DPP4 medications. The present study implemented a combined approach to machine learning and structure-based virtual screening to identify DPP4 inhibitors. Two ML models were trained based on DPP4 IC50 datasets. The ML models random forest (RF) and multilayer perceptron (MLP) neural network showed good accuracy, with the area under the curve being 0.93 and 0.91, respectively. The natural compound library was screened through ML models, and 1% (217) of compounds were selected for further screening. Structure-based virtual screening was performed along with positive control sitagliptin to obtain more specific and selective leads for DPP4. Based on binding affinity, drug-likeness properties, and interaction with DPP4, Z-614 and Z-997 compounds showed high binding affinity and specificity in the catalytic pocket of DPP4. Finally, the stability conformation of the DPP4 enzyme complex was checked by a molecular dynamics (MD) simulation. The MD simulation showed that both compounds bind better in the catalytic pocket, but the Z-614 compound altered the DPP4 native conformation. Therefore, Z-614 showed a high deviation in the backbone. This combined approach (ML and structure-based) study reported that Z-997 binds most stably to DPP4 in their catalytic pocket with a binding free energy of -70.3 kJ/mol, suggesting its therapeutic potential as a treatment option for T2DM disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahid Ali
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Sibhghatulla Shaikh
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
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35
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Cai L, Han F, Ji B, He X, Wang L, Niu T, Zhai J, Wang J. In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling. Molecules 2023; 28:8034. [PMID: 38138524 PMCID: PMC10745665 DOI: 10.3390/molecules28248034] [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: 11/07/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.
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Affiliation(s)
| | | | | | | | | | | | | | - Junmei Wang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.C.); (F.H.); (B.J.); (X.H.); (L.W.); (T.N.); (J.Z.)
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36
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Zhu Z, Yao Z, Zheng X, Qi G, Li Y, Mazur N, Gao X, Gong Y, Cong B. Drug-target affinity prediction method based on multi-scale information interaction and graph optimization. Comput Biol Med 2023; 167:107621. [PMID: 37907030 DOI: 10.1016/j.compbiomed.2023.107621] [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: 07/18/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023]
Abstract
Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability. Therefore, transformer and diffusion on drug graphs in DTA prediction (TDGraphDTA) are introduced to predict drug-target interactions using multi-scale information interaction and graph optimization. An interactive module is integrated into feature extraction of drug and target features at different granularity levels. A diffusion model-based graph optimization module is proposed to improve the representation of molecular graph structures and enhance the interpretability of graph representations while obtaining optimal feature representations. In addition, TDGraphDTA improves the accuracy and reliability of predictions by capturing relationships and contextual information between molecular substructures. The performance of the proposed TDGraphDTA in DTA prediction was verified on three publicly available benchmark datasets (Davis, Metz, and KIBA). Compared with state-of-the-art baseline models, it achieved better results in terms of consistency index, R-squared, etc. Furthermore, compared with some existing methods, the proposed TDGraphDTA is demonstrated to have better structure capturing capabilities by visualizing the feature capturing capabilities of the model using Grad-AAM toxicity labels in the ToxCast dataset. The corresponding source codes are available at https://github.com/Lamouryz/TDGraph.
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Affiliation(s)
- Zhiqin Zhu
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Zheng Yao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Xin Zheng
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Guanqiu Qi
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Yuanyuan Li
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Neal Mazur
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Xinbo Gao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Yifei Gong
- Faculty of applied science & engineering, the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto at Toronto, ON M5S, Canada.
| | - Baisen Cong
- Diagnostics Digital, DH(Shanghai) Diagnostics Co, Ltd, a Danaher company, Shanghai, 200335, China.
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37
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Meng Y, Wang Y, Xu J, Lu C, Tang X, Peng T, Zhang B, Tian G, Yang J. Drug repositioning based on weighted local information augmented graph neural network. Brief Bioinform 2023; 25:bbad431. [PMID: 38019732 PMCID: PMC10686358 DOI: 10.1093/bib/bbad431] [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: 08/26/2023] [Revised: 10/13/2023] [Accepted: 11/05/2023] [Indexed: 12/01/2023] Open
Abstract
Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.
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Affiliation(s)
- Yajie Meng
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Yi Wang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Road (S), Yuelu District, Changsha, Hunan Province 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Road (S), Yuelu District, Changsha, Hunan Province 410082, China
| | - Xianfang Tang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Tao Peng
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Bengong Zhang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Geng Tian
- Geneis Beijing Co., Ltd, No. 31, New North Road, Laiguanying, Chaoyang District, Beijing 100102, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd, No. 31, New North Road, Laiguanying, Chaoyang District, Beijing 100102, China
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38
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van Heerden A, Turon G, Duran-Frigola M, Pillay N, Birkholtz LM. Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds. ACS OMEGA 2023; 8:43813-43826. [PMID: 38027377 PMCID: PMC10666252 DOI: 10.1021/acsomega.3c05664] [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: 08/02/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023]
Abstract
Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds' molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.
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Affiliation(s)
- Ashleigh van Heerden
- Department
of Biochemistry, Genetics and Microbiology, Institute for Sustainable
Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
| | - Gemma Turon
- Ersilia
Open Source Initiative, 28 Belgrave Road, Cambridge CB1 3DE, U.K.
| | | | - Nelishia Pillay
- Department
of Computer Science, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
| | - Lyn-Marié Birkholtz
- Department
of Biochemistry, Genetics and Microbiology, Institute for Sustainable
Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa
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39
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Slabaugh G, Beltran L, Rizvi H, Deloukas P, Marouli E. Applications of machine and deep learning to thyroid cytology and histopathology: a review. Front Oncol 2023; 13:958310. [PMID: 38023130 PMCID: PMC10661921 DOI: 10.3389/fonc.2023.958310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.
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Affiliation(s)
- Greg Slabaugh
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
| | - Luis Beltran
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Hasan Rizvi
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Eirini Marouli
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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40
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Liu H, Yu S, Li X, Wang X, Qi D, Pan F, Chai X, Wang Q, Pan Y, Zhang L, Liu Y. Integration of Deep Learning and Sequential Metabolism to Rapidly Screen Dipeptidyl Peptidase (DPP)-IV Inhibitors from Gardenia jasminoides Ellis. Molecules 2023; 28:7381. [PMID: 37959800 PMCID: PMC10649927 DOI: 10.3390/molecules28217381] [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: 10/01/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Traditional Chinese medicine (TCM) possesses unique advantages in the management of blood glucose and lipids. However, there is still a significant gap in the exploration of its pharmacologically active components. Integrated strategies encompassing deep-learning prediction models and active validation based on absorbable ingredients can greatly improve the identification rate and screening efficiency in TCM. In this study, the affinity prediction of 11,549 compounds from the traditional Chinese medicine system's pharmacology database (TCMSP) with dipeptidyl peptidase-IV (DPP-IV) based on a deep-learning model was firstly conducted. With the results, Gardenia jasminoides Ellis (GJE), a food medicine with homologous properties, was selected as a model drug. The absorbed components of GJE were subsequently identified through in vivo intestinal perfusion and oral administration. As a result, a total of 38 prototypical absorbed components of GJE were identified. These components were analyzed to determine their absorption patterns after intestinal, hepatic, and systemic metabolism. Virtual docking and DPP-IV enzyme activity experiments were further conducted to validate the inhibitory effects and potential binding sites of the common constituents of deep learning and sequential metabolism. The results showed a significant DPP-IV inhibitory activity (IC50 53 ± 0.63 μg/mL) of the iridoid glycosides' potent fractions, which is a novel finding. Genipin 1-gentiobioside was screened as a promising new DPP-IV inhibitor in GJE. These findings highlight the potential of this innovative approach for the rapid screening of active ingredients in TCM and provide insights into the molecular mechanisms underlying the anti-diabetic activity of GJE.
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Affiliation(s)
- Huining Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Shuang Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xueyan Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xinyu Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Dongying Qi
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Fulu Pan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xiaoyu Chai
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Qianqian Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Yanli Pan
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Lei Zhang
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
| | - Yang Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
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Jabin T, Biswas S, Islam S, Sarker S, Afroze M, Paul GK, Razu MH, Monirruzzaman M, Huda M, Rahman M, Kundu NK, Kamal S, Karmakar P, Islam MA, Saleh MA, Khan M, Zaman S. Effects of gamma-radiation on microbial, nutritional, and functional properties of Katimon mango peels: A combined biochemical and in silico studies. Heliyon 2023; 9:e21556. [PMID: 38027912 PMCID: PMC10665690 DOI: 10.1016/j.heliyon.2023.e21556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 10/18/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Gamma radiation has notable impacts on the flesh of mangoes. In this research, Katimon mangoes were subjected to different levels of irradiation (0.5, 1.0, 1.5, and 2.0 kGy) using a60Co irradiator. The results showed that irradiation significantly reduced the microbial population in the mango peels, with the 1.5 kGy dose showing the most significant reduction. Irradiation also delayed ripening and extended the shelf life of the mango peels. The total fat, protein, ash, moisture, and sugar content of the mango peels were all affected by irradiation. The total protein content, ash content and moisture content increased after irradiation, while the fat content remained relatively unchanged. The sugar content increased in all samples after storage, but the non-irradiated samples had higher sugar levels than the irradiated ones. The dietary fiber content of the mango peels was not significantly affected by irradiation. The vitamin C content decreased in all samples after storage. The titratable acidity and total soluble solids content of the mango peels increased after storage, but there were no significant differences between the irradiated and non-irradiated samples. Antioxidant activity and cytotoxicity assessment highlighted the antioxidant potential and reduced toxicity of irradiated samples. Additionally, the antimicrobial effectiveness of irradiated mango peels was evaluated. The most substantial inhibitory zones (measuring 16.90 ± 0.35) against Pseudomonas sp. were observed at a radiation dose of 1.5 kGy with 150 μg/disc. To identify potential antimicrobial agents, the volatile components of mangoes irradiated with 1.5 kGy were analyzed through GC-MS. Subsequently, these compounds were subjected to in silico studies against a viable protein, TgpA, of Pseudomonas sp. (PDB ID: 6G49). Based on molecular dynamic simulations and ADMET properties, (-)-Carvone (-6.2), p-Cymene (-6.1), and Acetic acid phenylmethyl ester (-6.1) were identified as promising compounds for controlling Pseudomonas sp.
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Affiliation(s)
- Tabassum Jabin
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Suvro Biswas
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
| | - Shirmin Islam
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
| | - Swagotom Sarker
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Mirola Afroze
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Gobindo Kumar Paul
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
| | - Mamudul Hasan Razu
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Md Monirruzzaman
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Mainul Huda
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Mashiur Rahman
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Nayan Kumer Kundu
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Sabiha Kamal
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Pranab Karmakar
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Md Ariful Islam
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
| | - Md Abu Saleh
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
| | - Mala Khan
- Bangladesh Reference Institute for Chemical Measurements (BRiCM), Dhaka, Bangladesh
| | - Shahriar Zaman
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Bangladesh
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Lu Y, Chen Z, Pan Y, Qi F. Identification of Drug Compounds for Capsular Contracture Based on Text Mining and Deep Learning. Plast Reconstr Surg 2023; 152:779e-790e. [PMID: 36862957 DOI: 10.1097/prs.0000000000010350] [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] [Indexed: 03/04/2023]
Abstract
BACKGROUND Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear, and the effectiveness of nonsurgical treatment is still doubtful. The authors' study aimed to investigate new drug therapies for capsular contracture by using computational methods. METHODS Genes related to capsular contracture were identified by text mining and GeneCodis. Then, the candidate key genes were selected through protein-protein interaction analysis in Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. Drugs targeting the candidate genes with relation to capsular contracture were screened out in Pharmaprojects. Based on the drug-target interaction analysis by DeepPurpose, candidate drugs with highest predicted binding affinity were obtained eventually. RESULTS The authors' study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated eight candidate genes. One hundred drugs targeting the candidate genes were selected. The seven candidate drugs with the highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha antagonist, estrogen receptor agonist, insulin-like growth factor 1 receptor, tyrosine kinase inhibitor, and matrix metallopeptidase 1 inhibitor. CONCLUSION Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring nonsurgical treatment to capsular contracture. CLINICAL QUESTION/LEVEL OF EVIDENCE Therapeutic, V.
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Affiliation(s)
- Yeheng Lu
- From the Department of Plastic Surgery, Zhongshan Hospital
| | - Zhiwei Chen
- Big Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University
| | - Yuyan Pan
- From the Department of Plastic Surgery, Zhongshan Hospital
| | - Fazhi Qi
- From the Department of Plastic Surgery, Zhongshan Hospital
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Shan W, Chen L, Xu H, Zhong Q, Xu Y, Yao H, Lin K, Li X. GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47. Front Chem 2023; 11:1292869. [PMID: 37927570 PMCID: PMC10623438 DOI: 10.3389/fchem.2023.1292869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.
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Affiliation(s)
- Wenying Shan
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Lvqi Chen
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hao Xu
- Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, Nanjing, China
- National Engineering Laboratory for Biomass Chemical Utilization, Nanjing, China
| | - Qinghao Zhong
- School of Humanities and Social Sciences, The Chinese University of Hong Kong, Shenzhen, China
| | - Yinqiu Xu
- Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xuanyi Li
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
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Cui S, Gao Y, Huang Y, Shen L, Zhao Q, Pan Y, Zhuang S. Advances and applications of machine learning and deep learning in environmental ecology and health. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122358. [PMID: 37567408 DOI: 10.1016/j.envpol.2023.122358] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.
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Affiliation(s)
- Shixuan Cui
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yuchen Gao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yizhou Huang
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Lilai Shen
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qiming Zhao
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yaru Pan
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shulin Zhuang
- Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
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Omar A, Abd El-Hafeez T. Quantum computing and machine learning for Arabic language sentiment classification in social media. Sci Rep 2023; 13:17305. [PMID: 37828056 PMCID: PMC10570340 DOI: 10.1038/s41598-023-44113-7] [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: 04/29/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.
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Affiliation(s)
- Ahmed Omar
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
| | - Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
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Sabei FY, Y Safhi A, Almoshari Y, Salawi A, H Sultan M, Ali Bakkari M, Alsalhi A, A Madkhali O, M Jali A, Ahsan W. Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies. J Biomol Struct Dyn 2023:1-12. [PMID: 37776007 DOI: 10.1080/07391102.2023.2263588] [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: 04/03/2023] [Accepted: 08/28/2023] [Indexed: 10/01/2023]
Abstract
The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fahad Y Sabei
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Awaji Y Safhi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Yosif Almoshari
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Ahmad Salawi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Muhammad H Sultan
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Mohammed Ali Bakkari
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdullah Alsalhi
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Osama A Madkhali
- Department of Pharmaceutics, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdulmajeed M Jali
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Waquar Ahsan
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
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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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Li X, Yang Q, Luo G, Xu L, Dong W, Wang W, Dong S, Wang K, Xuan P, Gao X. SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions. BIOINFORMATICS ADVANCES 2023; 3:vbad116. [PMID: 38282612 PMCID: PMC10818136 DOI: 10.1093/bioadv/vbad116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/31/2023] [Accepted: 08/24/2023] [Indexed: 01/30/2024]
Abstract
Motivation Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life. Availability and implementation The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.
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Affiliation(s)
- Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Gongning Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Weihe Dong
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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Provasi D, Filizola M. Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552065. [PMID: 37609329 PMCID: PMC10441297 DOI: 10.1101/2023.08.04.552065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, utilizing these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning using combined ligand-based and structure-based molecular descriptors from the entire opioid receptor (OR) subfamily in building robust deep learning models for enhanced bioactivity prediction of opioid ligands at each individual OR subtype. Our studies hold the potential to greatly advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics.
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Binatlı OC, Gönen M. MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding. BMC Bioinformatics 2023; 24:276. [PMID: 37407927 DOI: 10.1186/s12859-023-05401-1] [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: 03/22/2023] [Accepted: 06/25/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously. RESULTS We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .
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Affiliation(s)
- Oğuz C Binatlı
- Graduate School of Sciences and Engineering, Koç University, 34450, Istanbul, Turkey
| | - Mehmet Gönen
- Department of Industrial Engineering, College of Engineering, Koç University, 34450, Istanbul, Turkey.
- School of Medicine, Koç University, 34450, Istanbul, Turkey.
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