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Zhang R, Nolte D, Sanchez-Villalobos C, Ghosh S, Pal R. Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling. Nat Commun 2024; 15:5072. [PMID: 38871711 DOI: 10.1038/s41467-024-49372-0] [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: 05/13/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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Affiliation(s)
- Ruibo Zhang
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Daniel Nolte
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Cesar Sanchez-Villalobos
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NB, 68588, USA.
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
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2
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Luo Y, Li S, Peng L, Ding P, Liang W. Predicting associations between drugs and G protein-coupled receptors using a multi-graph convolutional network. Comput Biol Chem 2024; 110:108060. [PMID: 38579550 DOI: 10.1016/j.compbiolchem.2024.108060] [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: 09/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024]
Abstract
Developing new drugs is an expensive, time-consuming process that frequently involves safety concerns. By discovering novel uses for previously verified drugs, drug repurposing helps to bypass the time-consuming and costly process of drug development. As the largest family of proteins targeted by verified drugs, G protein-coupled receptors (GPCR) are vital to efficiently repurpose drugs by inferring their associations with drugs. Drug repurposing may be sped up by computational models that predict the strength of novel drug-GPCR pairs interaction. To this end, a number of models have been put forth. In existing methods, however, drug structure, drug-drug interactions, GPCR sequence, and subfamily information couldn't simultaneously be taken into account to detect novel drugs-GPCR relationships. In this study, based on a multi-graph convolutional network, an end-to-end deep model was developed to efficiently and precisely discover latent drug-GPCR relationships by combining data from multi-sources. We demonstrated that our model, based on multi-graph convolutional networks, outperformed rival deep learning techniques as well as non-deep learning models in terms of inferring drug-GPCR relationships. Our results indicated that integrating data from multi-sources can lead to further advancement.
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Affiliation(s)
- Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
| | - Shasha Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China.
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, Hunan 421001, China
| | - Wei Liang
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China.
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3
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Yellurkar ML, Prasanna VS, Das P, Sarkar S, Matta R, Dhaked DK, Peraman R, Taraphdar AK, Nanjappan SK, Velayutham R, Arumugam S. Indigenous wisdom of a Kwatha to treat NASH: An insight into the mechanism. JOURNAL OF ETHNOPHARMACOLOGY 2024; 326:117935. [PMID: 38408692 DOI: 10.1016/j.jep.2024.117935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/08/2024] [Accepted: 02/17/2024] [Indexed: 02/28/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Nonalcoholic fatty liver disease (NAFLD) is the most common severe liver disease globally, progressing further into nonalcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Vasaguduchyadi Kwatha (VK) is an Ayurvedic formulation traditionally used to treat liver diseases and other metabolic complications. This study is an ethnopharmacological approach to unravel this indigenous remedy. AIM OF THE STUDY We aimed to discover the probable mechanism of action of VK against NASH in this study, using network pharmacology, molecular docking, in vitro study, and preclinical investigation. METHODS AND RESULTS Among the 55 components identified, 10 were confirmed based on mass, elution charecteristics, MS/MS analysis data, and fragmentation rules. Computational study indicated 92 targets involved in the central pathways of NASH, out of which only 15 targets and 9 VK constituents have significant docking scores. In vitro and in vivo analysis results showed that VK significantly reduces weight gain and improves insulin sensitivity, dyslipidemia, steatohepatitis and overall histological features of NASH compared to saroglitazar (SGZR). CONCLUSION Our detailed study yielded three signalling pathways related to NASH on which VK has maximum effect, bringing up a probable alternative treatment for NASH.
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Affiliation(s)
- Manoj Limbraj Yellurkar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Vani Sai Prasanna
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Pamelika Das
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Sulogna Sarkar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Rakesh Matta
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Devendra Kumar Dhaked
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Ramalingam Peraman
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Export Promotion Industrial Park (EPIP) Zandaha Road, NH322, Hajipur, Bihar, 844102, India
| | - Amit Kumar Taraphdar
- Department of Dravyaguna (Ayurvedic Pharmacology), Institute of Post Graduate Ayurvedic Education and Research, 294/3/1, Acharya Prafulla Chandra Road, Kolkata, 700009, West Bengal, India
| | - Satheesh Kumar Nanjappan
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India
| | - Ravichandiran Velayutham
- Department of Natural Products, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India.
| | - Somasundaram Arumugam
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Chunilal Bhawan, 168 Maniktala Main Road, Kolkata, 700054, West Bengal, India.
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4
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Hu H, Serra C, Zhang W, Scrivo A, Fernández-Carasa I, Consiglio A, Aytes A, Pujana MA, Llebaria A, Antolin AA. Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite. Cell Chem Biol 2024; 31:973-988.e4. [PMID: 38335967 DOI: 10.1016/j.chembiol.2024.01.007] [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: 11/21/2022] [Revised: 08/16/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.
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Affiliation(s)
- Huabin Hu
- Center for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London SM2 5NG, UK
| | - Carme Serra
- Medicinal Chemistry and Synthesis (MCS) Laboratory, Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain; Synthesis of High Added Value Molecules (SIMChem), Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain
| | - Wenjie Zhang
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Aurora Scrivo
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
| | - Irene Fernández-Carasa
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain
| | - Antonella Consiglio
- Department of Pathology and Experimental Therapeutics, Bellvitge University Hospital-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain; Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona, Spain; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alvaro Aytes
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Miguel Angel Pujana
- ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain
| | - Amadeu Llebaria
- Medicinal Chemistry and Synthesis (MCS) Laboratory, Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain; Synthesis of High Added Value Molecules (SIMChem), Institut de Química Avançada de Catalunya (IQAC-CSIC), 08034 Barcelona, Spain.
| | - Albert A Antolin
- Center for Cancer Drug Discovery, Division of Cancer Therapeutics, The Institute of Cancer Research, London SM2 5NG, UK; ProCURE, Catalan Institute of Oncology (ICO), Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Catalonia, Spain.
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5
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Daina A, Zoete V. Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Commun Chem 2024; 7:105. [PMID: 38724725 PMCID: PMC11082207 DOI: 10.1038/s42004-024-01179-2] [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: 04/06/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.
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Affiliation(s)
- Antoine Daina
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland
| | - Vincent Zoete
- Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
- Computer-Aided Molecular Engineering, Department of Oncology UNIL-CHUV, Ludwig Institute for Cancer Research Lausanne Branch, University of Lausanne, Lausanne, Switzerland.
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6
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Feng F, Zhang Y, Zhang X, Mu B, Qu W, Wang P. Natural Nano-Minerals (NNMs): Conception, Classification and Their Biomedical Composites. ACS OMEGA 2024; 9:17760-17783. [PMID: 38680370 PMCID: PMC11044256 DOI: 10.1021/acsomega.4c00674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/15/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024]
Abstract
Natural nano-minerals (NNMs) are minerals that are derived from nature with a size of less than 100 nm in at least one dimension in size. NNMs have a number of excellent properties due to their unique nanostructure and have been applied in various fields in recent years. They are rising stars in various disciplines, such as materials, biomedicine, and chemistry, taking advantage of their huge surface area, multiple active sites, excellent adsorption capacity, large quantity, low cost, and nontoxicity, etc. To provide a more comprehensive overview of NNMs and the biomedical applications of NNMs-based nanocomposites, this review classifies NNMs into three types by dimension, lists the structure and properties of typical NNMs, and illustrates their biomedical applications. Furthermore, a novel concept of natural nanomineral medical materials (NNMMs) is proposed, focusing on the medical value of NNMs. In addition, this review attempts to address the current challenges and delineate future directions for the advancement of NNMs. With the deepening of biomedical applications, it is believed that NNMMMs will inevitably play an important role in the field of human health and contribute to its promotion.
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Affiliation(s)
- Feng Feng
- Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Materials Science and Technology, China University of Geosciences (Beijing), Beijing100083, China
| | - Yihe Zhang
- Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Materials Science and Technology, China University of Geosciences (Beijing), Beijing100083, China
| | - Xiao Zhang
- Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Materials Science and Technology, China University of Geosciences (Beijing), Beijing100083, China
| | - Bin Mu
- Key Laboratory of Clay Mineral Applied Research of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Wenjie Qu
- Engineering Research Center of Ministry of Education for Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Materials Science and Technology, China University of Geosciences (Beijing), Beijing100083, China
| | - Peixia Wang
- National Anti-Drug Laboratory Beijing Regional Center, Beijing, 100164, China
- Beijing Narcotics Control Technology Center, Beijing, 100164, China
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7
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Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2024:gkae303. [PMID: 38647086 DOI: 10.1093/nar/gkae303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.
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Affiliation(s)
- Priyanka Banerjee
- Institute for Physiology & Science-IT, Charité - University Medicine Berlin, 10115 Berlin, Germany
- Member of the KFO 339: Food Allergy and Tolerance (Food@), Clinical Research Unit funded by the German Research Foundation, Berlin, Germany
| | - Emanuel Kemmler
- Institute for Physiology & Science-IT, Charité - University Medicine Berlin, 10115 Berlin, Germany
- Member of the KFO 339: Food Allergy and Tolerance (Food@), Clinical Research Unit funded by the German Research Foundation, Berlin, Germany
| | - Mathias Dunkel
- Institute for Physiology & Science-IT, Charité - University Medicine Berlin, 10115 Berlin, Germany
| | - Robert Preissner
- Institute for Physiology & Science-IT, Charité - University Medicine Berlin, 10115 Berlin, Germany
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8
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An H, Zhang M, Gu Z, Jiao X, Ma Y, Huang Z, Wen Y, Dong Y, Zhang P. Advances in Polysaccharides for Cartilage Tissue Engineering Repair: A Review. Biomacromolecules 2024; 25:2243-2260. [PMID: 38523444 DOI: 10.1021/acs.biomac.3c01424] [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: 03/26/2024]
Abstract
Cartilage repair has been a significant challenge in orthopedics that has not yet been fully resolved. Due to the absence of blood vessels and the almost cell-free nature of mature cartilage tissue, the limited ability to repair cartilage has resulted in significant socioeconomic pressures. Polysaccharide materials have recently been widely used for cartilage tissue repair due to their excellent cell loading, biocompatibility, and chemical modifiability. They also provide a suitable microenvironment for cartilage repair and regeneration. In this Review, we summarize the techniques used clinically for cartilage repair, focusing on polysaccharides, polysaccharides for cartilage repair, and the differences between these and other materials. In addition, we summarize the techniques of tissue engineering strategies for cartilage repair and provide an outlook on developing next-generation cartilage repair and regeneration materials from polysaccharides. This Review will provide theoretical guidance for developing polysaccharide-based cartilage repair and regeneration materials with clinical applications for cartilage tissue repair and regeneration.
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Affiliation(s)
- Heng An
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Meng Zhang
- Department of Orthopaedics and Trauma Peking University People's Hospital, Beijing 100044, China
| | - Zhen Gu
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiangyu Jiao
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yinglei Ma
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhe Huang
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yongqiang Wen
- Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | | | - Peixun Zhang
- Department of Orthopaedics and Trauma Peking University People's Hospital, Beijing 100044, China
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9
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Zhang Y, Guo J, Liu Y, Qu Y, Li YQ, Mu Y, Li W. An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase. Int J Biol Macromol 2024; 265:130644. [PMID: 38462102 DOI: 10.1016/j.ijbiomac.2024.130644] [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: 10/26/2023] [Revised: 02/25/2024] [Accepted: 03/04/2024] [Indexed: 03/12/2024]
Abstract
The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.
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Affiliation(s)
- Yunju Zhang
- School of Physics, Shandong University, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Yang Liu
- School of Physics, Shandong University, China
| | - Yuanyuan Qu
- School of Physics, Shandong University, China
| | | | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
| | - Weifeng Li
- School of Physics, Shandong University, China.
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10
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Wang M, Wang J, Rong Z, Wang L, Xu Z, Zhang L, He J, Li S, Cao L, Hou Y, Li K. A bidirectional interpretable compound-protein interaction prediction framework based on cross attention. Comput Biol Med 2024; 172:108239. [PMID: 38460309 DOI: 10.1016/j.compbiomed.2024.108239] [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: 08/31/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.
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Affiliation(s)
- Meng Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jianmin Wang
- School of Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea
| | - Zhiwei Rong
- School of Public Health, Peking University, Beijing, 100871, China
| | - Liuying Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Zhenyi Xu
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Liuchao Zhang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jia He
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Shuang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Lei Cao
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Yan Hou
- School of Public Health, Peking University, Beijing, 100871, China
| | - Kang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China.
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11
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Jeong E, Hong H, Lee YA, Kim KS. Potential Rheumatoid Arthritis-Associated Interstitial Lung Disease Treatment and Computational Approach for Future Drug Development. Int J Mol Sci 2024; 25:2682. [PMID: 38473928 PMCID: PMC11154459 DOI: 10.3390/ijms25052682] [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: 01/22/2024] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and showed efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with anti-fibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild anti-fibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.
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Affiliation(s)
- Eunji Jeong
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
| | - Hyunseok Hong
- Yale College, Yale University, New Haven, CT 06520, USA;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeon-Ah Lee
- Division of Rheumatology, Department of Internal Medicine, Kyung Hee University Hospital, Seoul 02447, Republic of Korea;
| | - Kyoung-Soo Kim
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- East-West Bone & Joint Disease Research Institute, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea
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12
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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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13
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Wang W, Shuai Y, Yang Q, Zhang F, Zeng M, Li M. A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches. Brief Bioinform 2024; 25:bbae050. [PMID: 38388682 PMCID: PMC10883809 DOI: 10.1093/bib/bbae050] [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: 12/13/2023] [Revised: 01/17/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.
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Affiliation(s)
- Wenkang Wang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Yunyan Shuai
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Qiurong Yang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
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14
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [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: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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15
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Wei J, Lu L, Shen T. Predicting drug-protein interactions by preserving the graph information of multi source data. BMC Bioinformatics 2024; 25:10. [PMID: 38177981 PMCID: PMC10768380 DOI: 10.1186/s12859-023-05620-6] [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] [Accepted: 12/15/2023] [Indexed: 01/06/2024] Open
Abstract
Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.
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Affiliation(s)
- Jiahao Wei
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China
| | - Linzhang Lu
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China.
- School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China.
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guizhou, 550001, China.
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16
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Yu Z, Wu Z, Zhou M, Chen L, Li W, Liu G, Tang Y. mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events. Comput Biol Med 2024; 168:107831. [PMID: 38081118 DOI: 10.1016/j.compbiomed.2023.107831] [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: 08/12/2023] [Revised: 11/23/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Long Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
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17
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Abdul Raheem AK, Dhannoon BN. Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview. Curr Drug Discov Technol 2024; 21:e010923220652. [PMID: 37680152 DOI: 10.2174/1570163820666230901160043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/29/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.
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Affiliation(s)
- Ali K Abdul Raheem
- Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq
- University of Warith Al-Anbiyaa, Kerbala, Iraq
| | - Ban N Dhannoon
- Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
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18
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Vasan K, Gysi DM, Barabási AL. The clinical trials puzzle: How network effects limit drug discovery. iScience 2023; 26:108361. [PMID: 38146432 PMCID: PMC10749231 DOI: 10.1016/j.isci.2023.108361] [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: 04/19/2023] [Revised: 09/04/2023] [Accepted: 10/25/2023] [Indexed: 12/27/2023] Open
Abstract
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.
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Affiliation(s)
- Kishore Vasan
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Statistics, Federal University of Parana, Curtiba, Brazil
- Department of Veteran Affairs, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Veteran Affairs, Boston, MA, USA
- Department of Data and Network Science, Central European University, Budapest, Hungary
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19
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Sun X, Zhang P, Ye Z, Li L, Li Q, Zhang H, Liu B, Gui L. A Soft Capsule for Magnetically Driven Drug Delivery Based on a Hard-Magnetic Elastomer Foam. ACS Biomater Sci Eng 2023; 9:6915-6925. [PMID: 37527429 DOI: 10.1021/acsbiomaterials.3c00710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Drug delivery systems based on porous soft biomaterials have been widely reported because of stimuli-responsive drug release and their inherent reservoirs for drug storage. Especially, magnetic-responsive porous soft biomaterials achieve rapid and real-time control of drug release due to the magnetic field-triggered large deformation. However, the drug release profiles of these materials are difficult to predict and repeat, which restrict them from releasing drugs in the required dosage. Here, we report a soft capsule based on a flexible hard-magnetic elastomer foam (HEF) for magnetically controlled on-demand drug delivery. The HEF capsule contains an inner HEF and an outer elastomer shell. The HEF exhibits low elastic modulus (10 kPa) and highly interconnected pores (81% interconnected pores). Benefitting from the novel precompressed magnetization, the compressive deformation of HEF reaches 66%. Thus, an adjustable drug release rate ranging from 0.02 to 1.7 mL/min in the HEF capsule is achieved. The deformation-triggered drug release profiles of the HEF capsule under the magnetic field are accurately predicted, allowing 85% accuracy in drug dosage regulation and more than 90% maximum cumulative drug release. Especially, the HEF capsule is proven capable of acting as a soft robot to perform magnetically driven drug delivery in a human stomach model. HEF can potentially serve as a soft robot for biomedical applications in the human body.
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Affiliation(s)
- Xiao Sun
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China
| | - Pan Zhang
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China
| | - Zi Ye
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Lei Li
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Beijing 100144, China
| | - Qian Li
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100039, China
| | - Huimin Zhang
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100039, China
| | - Bingxin Liu
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China
| | - Lin Gui
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China
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20
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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21
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Kumar RN, Prasanth D, Midthuri PG, Ahmad SF, Badarinath AV, Karumanchi SK, Seemaladinne R, Nalluri R, Pasala PK. Unveiling the Cardioprotective Power: Liquid Chromatography-Mass Spectrometry (LC-MS)-Analyzed Neolamarckia cadamba (Roxb.) Bosser Leaf Ethanolic Extract against Myocardial Infarction in Rats and In Silico Support Analysis. PLANTS (BASEL, SWITZERLAND) 2023; 12:3722. [PMID: 37960078 PMCID: PMC10650531 DOI: 10.3390/plants12213722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023]
Abstract
Neolamarckia cadamba (Roxb.) Bosser, a member of the Rubiaceae family, is a botanical species with recognized therapeutic properties. It is commonly used in traditional medicine to treat cardiac ailments and other disorders. However, the precise active constituents and the potential mechanisms by which they manage cardiovascular disorders remain unclear. Therefore, this study aimed to ascertain the bioactive components and investigate their underlying mechanisms of action. N. cadamba is used to treat cardiovascular disorders using the integrated metabolomic methodology. An HPLC-QTOF-MS/MS analysis determined the potential chemicals in the N. cadamba leaf ethanol extract (NCEE). A thorough investigation of the NCEE samples used in this study led to the identification of 32 phytoconstituents. Of the 32 compounds, 19 obeyed Lipinski's rule of five (RO5). A molecular docking study directed towards HMG-CoA reductase used 19 molecules. The reference drug atorvastatin indicated a binding energy of -3.9 kcal/mol, while the other substances, Cinchonain Ib and Dukunolide B, revealed binding energies of -5.7 and -5.3 kcal/mol, respectively. Both phytocompounds showed no toxicity and exhibited favorable pharmacokinetic properties. In vivo study results concluded that treatment with NCEE significantly reduced the cardiac myocardial infarction (MI) marker CK-MB and atherogenic risk indices, such as the atherogenic index plasma (AIP), cardiac risk ratio (CRR), and atherogenic coefficient (AC) in isoproterenol-induced MI rats. In MI rats, NCEE therapy significantly improved the antioxidant system of the heart tissue, as evidenced by the increased levels of GSH and SOD, lower levels of the oxidative stress marker MDA, and significantly decreased HMG-CoA activity. Additionally, electrocardiogram (ECG) signals from rats treated with NCEE resembled those treated with traditional atorvastatin to treat myocardial infarction. This study used H&E staining to show that administering NCEE before treatment reduced cardiac myocyte degeneration in rats with myocardial infarction, increased the presence of intact nuclei, and increased myocardial fiber strength. The potential cardioprotective effect observed in myocardial infarction (MI) rats treated with NCEE can be extrapolated from computational data to be caused by Cinchonain Ib.
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Affiliation(s)
- Raghupathi Niranjan Kumar
- Department of Pharmacology, Santhiram College of Pharmacy, JNTUA, Nandyal 518112, Andhra Pradesh, India; (R.N.K.); (P.G.M.)
| | - Dsnbk Prasanth
- Department of Pharmacognosy, KVSR Siddhartha College of Pharmaceutical Sciences, Vijayawada 520010, Andhra Pradesh, India;
| | - Praisy Gladys Midthuri
- Department of Pharmacology, Santhiram College of Pharmacy, JNTUA, Nandyal 518112, Andhra Pradesh, India; (R.N.K.); (P.G.M.)
| | - Sheikh F. Ahmad
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | | | - Srikanth Kumar Karumanchi
- Department of Pharmaceutical Chemistry, DKSS Institute of Pharmaceutical Science & Research (for Girls), Swami-Chincholi, Bhigwan 413130, Maharashtra, India;
| | | | - Rahul Nalluri
- Department of Pharmacy, Texas A&M University, Kingsville, TX 78363, USA;
| | - Praveen Kumar Pasala
- Department of Pharmacology, Raghavendra Institute of Pharmaceutical Education and Research, JNTUA, Anantapuramu 515721, Andhra Pradesh, India
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22
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Ong WJG, Kirubakaran P, Karanicolas J. Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.04.556234. [PMID: 37732243 PMCID: PMC10508770 DOI: 10.1101/2023.09.04.556234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The extreme surge of interest over the past decade surrounding the use of neural networks has inspired many groups to deploy them for predicting binding affinities of drug-like molecules to their receptors. A model that can accurately make such predictions has the potential to screen large chemical libraries and help streamline the drug discovery process. However, despite reports of models that accurately predict quantitative inhibition using protein kinase sequences and inhibitors' SMILES strings, it is still unclear whether these models can generalize to previously unseen data. Here, we build a Convolutional Neural Network (CNN) analogous to those previously reported and evaluate the model over four datasets commonly used for inhibitor/kinase predictions. We find that the model performs comparably to those previously reported, provided that the individual data points are randomly split between the training set and the test set. However, model performance is dramatically deteriorated when all data for a given inhibitor is placed together in the same training/testing fold, implying that information leakage underlies the models' performance. Through comparison to simple models in which the SMILES strings are tokenized, or in which test set predictions are simply copied from the closest training set data points, we demonstrate that there is essentially no generalization whatsoever in this model. In other words, the model has not learned anything about molecular interactions, and does not provide any benefit over much simpler and more transparent models. These observations strongly point to the need for richer structure-based encodings, to obtain useful prospective predictions of not-yet-synthesized candidate inhibitors.
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Affiliation(s)
- Wern Juin Gabriel Ong
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
- Bowdoin College, Brunswick, ME 04011
| | - Palani Kirubakaran
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
| | - John Karanicolas
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
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23
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Huang T, Zhang Q, Yi J, Wang R, Zhang Z, Luo P, Zeng R, Wang Y, Tu M. PEG-Sheddable Nanodrug Remodels Tumor Microenvironment to Promote Effector T Cell Infiltration and Revise Their Exhaustion for Breast Cancer Immunotherapy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2301749. [PMID: 37211704 DOI: 10.1002/smll.202301749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/30/2023] [Indexed: 05/23/2023]
Abstract
Low infiltration of cytotoxic T lymphocytes and their exhaustion manifest the two concurrent main hurdles for achieving effective tumor immunotherapy of triple-negative breast cancer. It is found that Galectin-9 blockage can revise the exhaustion of effector T cells, meanwhile the repolarization of protumoral M2 tumor-associated macrophages (TAMs) into tumoricidal M1-like ones can recruit effector T cells infiltrating into tumor to boost immune responses. Herein, a sheddable PEG-decorated and M2-TAMs targeted nanodrug incorporating Signal Transducer and Activator of Transcription 6 inhibitor (AS) and anti-Galectin-9 antibody (aG-9) is prepared. The nanodrug responds to acidic tumor microenvironment (TME) with the shedding of PEG corona and the release of aG-9, exerting local blockade of PD-1/Galectin-9/TIM-3 interaction to augment effector T cells via exhaustion reversing. Synchronously, targeted repolarization of M2-TAMs into M1 phenotype by AS-loaded nanodrug is achieved, which promotes tumor infiltration of effector T cells and thus synergizes with aG-9 blockade to boost the therapeutic efficacy. Besides, the PEG-sheddable approach endows nanodrug with stealth ability to reduce immune-related adverse effects caused by AS and aG-9. This PEG sheddable nanodrug holds the potential to reverse the immunosuppressive TME and increase effector T cell infiltration, which dramatically enhances immunotherapy in highly malignant breast cancer.
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Affiliation(s)
- Tao Huang
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Qiaoyun Zhang
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Jing Yi
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Rongze Wang
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Zekun Zhang
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Pin Luo
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Rong Zeng
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Yong Wang
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
| | - Mei Tu
- College of Chemistry and Materials Science, Jinan University, Guangzhou, 510632, China
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24
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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25
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Choi J. Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner. iScience 2023; 26:106911. [PMID: 37305691 PMCID: PMC10250834 DOI: 10.1016/j.isci.2023.106911] [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: 04/14/2023] [Revised: 04/28/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023] Open
Abstract
Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.
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Affiliation(s)
- Juyoung Choi
- Department of Life Science, Sogang University, Seoul 04017, South Korea
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26
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Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling. J Cheminform 2023; 15:60. [PMID: 37296454 DOI: 10.1186/s13321-023-00728-6] [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: 09/16/2022] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R2 determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .
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Affiliation(s)
- Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Anna Fava
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Vincenzo Pisapia
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Francesco Sacco
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Daniela Iaconis
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
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27
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Srivathsa AV, Sadashivappa NM, Hegde AK, Radha S, Mahesh AR, Ammunje DN, Sen D, Theivendren P, Govindaraj S, Kunjiappan S, Pavadai P. A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery. Curr Pharm Des 2023; 29:1180-1192. [PMID: 37132148 DOI: 10.2174/1381612829666230428110542] [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: 11/30/2022] [Revised: 02/06/2023] [Accepted: 02/27/2023] [Indexed: 05/04/2023]
Abstract
Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.
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Affiliation(s)
- Anjana Vidya Srivathsa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Nandini Markuli Sadashivappa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Apeksha Krishnamurthy Hegde
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Srimathi Radha
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu, 603203, India
| | - Agasa Ramu Mahesh
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Damodar Nayak Ammunje
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Debanjan Sen
- Department of Pharmaceutical Chemistry, BCDA College of Pharmacy & Technology, Hridaypur, Kolkata, 700127, West Bengal, India
| | - Panneerselvam Theivendren
- Department of Pharmaceutical Chemistry, Swamy Vivekanandha College of Pharmacy, Elayampalayam, Tiruchengode, 637205, India
| | - Saravanan Govindaraj
- Department of Pharmaceutical Chemistry, MNR College of Pharmacy, Fasalwadi, Sangareddy, 502 001, India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
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28
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Huang T, Lin KH, Machado-Vieira R, Soares JC, Jiang X, Kim Y. Explainable drug side effect prediction via biologically informed graph neural network. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.26.23290615. [PMID: 37333107 PMCID: PMC10275013 DOI: 10.1101/2023.05.26.23290615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Early detection of potential side effects (SE) is a critical and challenging task for drug discovery and patient care. In-vitro or in-vivo approach to detect potential SEs is not scalable for many drug candidates during the preclinical stage. Recent advances in explainable machine learning may facilitate detecting potential SEs of new drugs before market release and elucidating the critical mechanism of biological actions. Here, we leverage multi-modal interactions among molecules to develop a biologically informed graph-based SE prediction model, called HHAN-DSI. HHAN-DSI predicted frequent and even uncommon SEs of the unseen drug with higher or comparable accuracy against benchmark methods. When applying HHAN-DSI to the central nervous system, the organs with the largest number of SEs, the model revealed diverse psychiatric medications' previously unknown but probable SEs, together with the potential mechanisms of actions through a network of genes, biological functions, drugs, and SEs.
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Affiliation(s)
- Tongtong Huang
- School of Biomedical Informatics, UTHealth, Houston, TX, United States
| | - Ko-Hong Lin
- School of Biomedical Informatics, UTHealth, Houston, TX, United States
| | - Rodrigo Machado-Vieira
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Jair C Soares
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, UTHealth, Houston, TX, United States
| | - Yejin Kim
- School of Biomedical Informatics, UTHealth, Houston, TX, United States
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29
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Pacureanu L, Bora A, Crisan L. New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods. Int J Mol Sci 2023; 24:ijms24119583. [PMID: 37298535 DOI: 10.3390/ijms24119583] [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: 05/01/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.
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Affiliation(s)
- Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
| | - Alina Bora
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
| | - Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
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30
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Ji KY, Liu C, Liu ZQ, Deng YF, Hou TJ, Cao DS. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? Brief Bioinform 2023; 24:6995377. [PMID: 36681902 DOI: 10.1093/bib/bbad014] [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: 11/07/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/23/2023] Open
Abstract
Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.
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Affiliation(s)
- Kai-Yue Ji
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Chong Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Zhao-Qian Liu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Ya-Feng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
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31
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An S, Hwang SY, Gong J, Ahn S, Park IG, Oh S, Chin YW, Noh M. Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis. J Chem Inf Model 2023; 63:856-869. [PMID: 36716271 DOI: 10.1021/acs.jcim.3c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.
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Affiliation(s)
- Seungchan An
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Seok Young Hwang
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Junpyo Gong
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Sungjin Ahn
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - In Guk Park
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Soyeon Oh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Young-Won Chin
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Minsoo Noh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
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32
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Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 2023; 24:38. [PMID: 36737694 PMCID: PMC9896467 DOI: 10.1186/s12859-023-05153-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.
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Affiliation(s)
- Lingzhi Hu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Chengzhou Fu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Zhonglu Ren
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Yongming Cai
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Jin Yang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Siwen Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Wenhua Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Deyu Tang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,grid.79703.3a0000 0004 1764 3838School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
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33
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Budak C, Mençik V, Gider V. Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method. J Biomol Struct Dyn 2023; 41:659-671. [PMID: 34877907 DOI: 10.1080/07391102.2021.2010601] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
COVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Cafer Budak
- Department of Biomedical Engineering, Dicle University, Diyarbakır, Turkey
| | - Vasfiye Mençik
- Department of Electric-Electronic Engineering, Dicle University, Diyarbakır, Turkey
| | - Veysel Gider
- Department of Electric-Electronic Engineering, Dicle University, Diyarbakır, Turkey
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Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs. Mar Drugs 2023; 21:md21020100. [PMID: 36827141 PMCID: PMC9961086 DOI: 10.3390/md21020100] [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: 12/21/2022] [Revised: 01/26/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.
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Using chemical and biological data to predict drug toxicity. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:53-64. [PMID: 36639032 DOI: 10.1016/j.slasd.2022.12.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/19/2022] [Accepted: 12/31/2022] [Indexed: 01/12/2023]
Abstract
Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.
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36
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Monir MK, Uddin MS, Sen S. Design of a novel photonic crystal fiber and numerical analysis of sensitivity for the detection of illegal drugs in terahertz regime. SENSING AND BIO-SENSING RESEARCH 2023. [DOI: 10.1016/j.sbsr.2023.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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37
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Feng F, Zhang W, Chai Y, Guo D, Chen X. Label-free target protein characterization for small molecule drugs: recent advances in methods and applications. J Pharm Biomed Anal 2023; 223:115107. [DOI: 10.1016/j.jpba.2022.115107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
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Rai A, Shah K, Dewangan HK. Review on the Artificial Intelligence-based Nanorobotics Targeted Drug Delivery System for Brain-specific Targeting. Curr Pharm Des 2023; 29:3519-3531. [PMID: 38111114 DOI: 10.2174/0113816128279248231210172053] [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: 09/28/2023] [Accepted: 11/07/2023] [Indexed: 12/20/2023]
Abstract
Contemporary medical research increasingly focuses on the blood-brain barrier (BBB) to maintain homeostasis in healthy individuals and provide solutions for neurological disorders, including brain cancer. Specialized in vitro modules replicate the BBB's complex structure and signalling using micro-engineered perfusion devices and advanced 3D cell cultures, thus advancing the understanding of neuropharmacology. This research explores nanoparticle-based biomolecular engineering for precise control, targeting, and transport of theranostic payloads across the BBB using nanorobots. The review summarizes case studies on delivering therapeutics for brain tumors and neurological disorders, such as Alzheimer's, Parkinson's, and multiple sclerosis. It also examines the advantages and disadvantages of nano-robotics. In conclusion, integrating machine learning and AI with robotics aims to develop safe nanorobots capable of interacting with the BBB without adverse effects. This comprehensive review is valuable for extensive analysis and is of great significance to healthcare professionals, engineers specializing in robotics, chemists, and bioengineers involved in pharmaceutical development and neurological research, emphasizing transdisciplinary approaches.
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Affiliation(s)
- Akriti Rai
- School of Pharmacy, Lingayas Vidyapeeth, Nachauli, Jasana Road, Faridabad, Haryana 121002, India
| | - Kamal Shah
- Institute of Pharmaceutical Research (IPR), GLA University Mathura, NH-2 Delhi Mathura Road, Po Chaumuhan, Mathura, Uttar Pradesh 281406, India
| | - Hitesh Kumar Dewangan
- University Institute of Pharma Sciences (UIPS), Chandigarh University, NH-95, Chandigarh Ludhiana Highway, Mohali, Punjab, India
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Santa Maria JP, Wang Y, Camargo LM. Perspective on the challenges and opportunities of accelerating drug discovery with artificial intelligence. FRONTIERS IN BIOINFORMATICS 2023; 3:1121591. [PMID: 36909937 PMCID: PMC9997711 DOI: 10.3389/fbinf.2023.1121591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Affiliation(s)
- John P Santa Maria
- Data and Translational Sciences, UCB Biosciences Inc., Cambridge, MA, United States
| | - Yuan Wang
- Data and Translational Sciences, UCB Biosciences Inc., Cambridge, MA, United States
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Liang X, Fu Y, Qu L, Zhang P, Chen Y. Prediction of drug side effects with transductive matrix co-completion. Bioinformatics 2023; 39:btad006. [PMID: 36655793 PMCID: PMC9869719 DOI: 10.1093/bioinformatics/btad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/06/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. RESULTS We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. AVAILABILITY AND IMPLEMENTATION The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xujun Liang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ying Fu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Lingzhi Qu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Pengfei Zhang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Yongheng Chen
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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Shan Y, Cheung L, Zhou Y, Huang Y, Huang RS. A systematic review on sex differences in adverse drug reactions related to psychotropic, cardiovascular, and analgesic medications. Front Pharmacol 2023; 14:1096366. [PMID: 37201021 PMCID: PMC10185891 DOI: 10.3389/fphar.2023.1096366] [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: 11/12/2022] [Accepted: 04/10/2023] [Indexed: 05/20/2023] Open
Abstract
Background and objective: Adverse drug reactions (ADRs) are the main safety concerns of clinically used medications. Accumulating evidence has shown that ADRs can affect men and women differently, which suggests sex as a biological predictor in the risk of ADRs. This review aims to summarize the current state of knowledge on sex differences in ADRs with the focus on the commonly used psychotropic, cardiovascular, and analgesic medications, and to aid clinical decision making and future mechanistic investigations on this topic. Methods: PubMed search was performed with combinations of the following terms: over 1,800 drugs of interests, sex difference (and its related terms), and side effects (and its related terms), which yielded over 400 unique articles. Articles related to psychotropic, cardiovascular, and analgesic medications were included in the subsequent full-text review. Characteristics and the main findings (male-biased, female-biased, or not sex biased ADRs) of each included article were collected, and the results were summarized by drug class and/or individual drug. Results: Twenty-six articles studying sex differences in ADRs of six psychotropic medications, ten cardiovascular medications, and one analgesic medication were included in this review. The main findings of these articles suggested that more than half of the ADRs being evaluated showed sex difference pattern in occurrence rate. For instance, lithium was found to cause more thyroid dysfunction in women, and amisulpride induced prolactin increase was more pronounced in women than in men. Some serious ADRs were also found to exert sex difference pattern, such as clozapine induced neutropenia was more prevalent in women whereas simvastatin/atorvastatin-related abnormal liver functions were more pronounced in men.
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43
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Galeano D, Paccanaro A. Machine learning prediction of side effects for drugs in clinical trials. CELL REPORTS METHODS 2022; 2:100358. [PMID: 36590692 PMCID: PMC9795366 DOI: 10.1016/j.crmeth.2022.100358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.
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Affiliation(s)
- Diego Galeano
- Department of Electronics and Mechatronics Engineering, Facultad de Ingeniería, Universidad Nacional de Asunción, San Lorenzo, Paraguay
| | - Alberto Paccanaro
- School of Applied Mathematics, Fundação Getulio Vargas, Rio de Janeiro, Brazil
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK
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In Silico Screening of Plant-Derived Anti-virals from Shorea hemsleyana (King) King ex Foxw Against SARS CoV-2 Main Protease. CHEMISTRY AFRICA 2022. [DOI: 10.1007/s42250-022-00521-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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45
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Structure-based virtual screening and molecular dynamics of potential inhibitors targeting sodium-bile acid co-transporter of carcinogenic liver fluke Clonorchis sinensis. PLoS Negl Trop Dis 2022; 16:e0010909. [DOI: 10.1371/journal.pntd.0010909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
Abstract
Background
Clonorchis sinensis requires bile acid transporters as this fluke inhabits bile juice-filled biliary ducts, which provide an extreme environment. Clonorchis sinensis sodium-bile acid co-transporter (CsSBAT) is indispensable for the fluke’s survival in the final host, as it circulates taurocholate and prevents bile toxicity in the fluke; hence, it is recognized as a useful drug target.
Methodology and principal findings
In the present study, using structure-based virtual screening approach, we presented inhibitor candidates targeting a bile acid-binding pocket of CsSBAT. CsSBAT models were built using tertiary structure modeling based on a bile acid transporter template (PDB ID: 3zuy and 4n7x) and were applied into AutoDock Vina for competitive docking simulation. First, potential compounds were identified from PubChem (holding more than 100,000 compounds) by applying three criteria: i) interacting more favorably with CsSBAT than with a human homolog, ii) intimate interaction to the inward- and outward-facing conformational states, iii) binding with CsSBAT preferably to natural bile acids. Second, two compounds were identified following the Lipinski’s rule of five. Third, other two compounds of molecular weight higher than 500 Da (Mr > 500 Da) were presumed to efficiently block the transporter via a feasible rational screening strategy. Of these candidates, compound 9806452 exhibited the least hepatotoxicity that may enhance drug-likeness properties.
Conclusions
It is proposed that compound 9806452 act as a potential inhibitor toward CsSBAT and further studies are warranted for drug development process against clonorchiasis.
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Small bioactive molecules designed to be probes as baits “fishing out” cellular targets: finding the fish in the proteome sea. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Yue R, Dutta A. Computational systems biology in disease modeling and control, review and perspectives. NPJ Syst Biol Appl 2022; 8:37. [PMID: 36192551 PMCID: PMC9528884 DOI: 10.1038/s41540-022-00247-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/05/2022] [Indexed: 02/02/2023] Open
Abstract
Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.
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Affiliation(s)
- Rongting Yue
- Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, Storrs, CT, 06269, USA.
| | - Abhishek Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, Storrs, CT, 06269, USA
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Wang FS, Chen PR, Chen TY, Zhang HX. Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220633. [PMID: 36303939 PMCID: PMC9597175 DOI: 10.1098/rsos.220633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of (UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.
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Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Ting-Yu Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Hao-Xiang Zhang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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Pu Y, Li J, Tang J, Guo F. DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2760-2769. [PMID: 34379594 DOI: 10.1109/tcbb.2021.3103966] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies for this task obviously have great benefits in reducing the drug search space. Most of computational methods to predict DTI are proposed to solve a binary classification problem, which ignore the influence of binding strength. Therefore, drug-target binding affinity prediction is still a challenging issue. Currently, lots of studies only extract sequence information that lacks feature-rich representation, but we consider more spatial features in order to merge various data in drug and target spaces. In this study, we propose a two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, via various information analysis modules. First stage is to utilize sequence and structure information to generate fusion feature map of candidate protein and drug pair through various analysis modules based deep learning. Second stage is to apply bagging-based ensemble learning strategy for regression prediction, and we obtain outstanding results by combining the advantages of various algorithms in efficient feature abstraction and regression calculation. Importantly, we evaluate our novel method, DeepFusionDTA, which delivers 1.5 percent CI increase on KIBA dataset and 1.0 percent increase on Davis dataset, by comparing with existing prediction tools, DeepDTA. Furthermore, the ideas we have offered can be applied to in-silico screening of the interaction space, to provide novel DTIs which can be experimentally pursued. The codes and data are available from https://github.com/guofei-tju/DeepFusionDTA.
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Sánchez-Ruiz A, Colmenarejo G. Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps. J Chem Inf Model 2022; 62:3734-3751. [PMID: 35938782 DOI: 10.1021/acs.jcim.2c00888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here, we retrieve and analyze the complete set of published interactions of food compounds with human proteins using the FooDB as a compound set and ChEMBL as a source of interactions. The data are analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment for the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well-established cheminformatic approaches [similarity ensemble approach (SEA) combined with the maximum Tanimoto coefficient to the nearest bioactive, "SEA + TC"], we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these data sets and analyses, we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.
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Affiliation(s)
- Andrés Sánchez-Ruiz
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, E28049 Madrid, Spain
| | - Gonzalo Colmenarejo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, E28049 Madrid, Spain
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