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Rajput A, Bhamare KT, Thakur A, Kumar M. Anti-Biofilm: Machine Learning Assisted Prediction of IC 50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing. J Mol Biol 2023; 435:168115. [PMID: 37356913 DOI: 10.1016/j.jmb.2023.168115] [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: 12/01/2022] [Revised: 04/06/2023] [Accepted: 04/14/2023] [Indexed: 06/27/2023]
Abstract
Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC50) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.
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Affiliation(s)
- Akanksha Rajput
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India
| | - Kailash T Bhamare
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Anamika Thakur
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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2
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AbdulHameed MDM, Liu R, Wallqvist A. Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors. ACS OMEGA 2023; 8:21853-21861. [PMID: 37360478 PMCID: PMC10286257 DOI: 10.1021/acsomega.3c01583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023]
Abstract
The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.
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Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Ruifeng Liu
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Anders Wallqvist
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
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3
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Akinola LK, Uzairu A, Shallangwa GA, Abechi SE. Development of binary classification models for grouping hydroxylated polychlorinated biphenyls into active and inactive thyroid hormone receptor agonists. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:267-284. [PMID: 37139950 DOI: 10.1080/1062936x.2023.2207039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Some adverse effects of hydroxylated polychlorinated biphenyls (OH-PCBs) in humans are presumed to be initiated via thyroid hormone receptor (TR) binding. Due to the trial-and-error approach adopted for OH-PCB selection in previous studies, experiments designed to test the TR binding hypothesis mostly utilized inactive OH-PCBs, leading to considerable waste of time, effort and other material resources. In this paper, linear discriminant analysis (LDA) and binary logistic regression (LR) were used to develop classification models to group OH-PCBs into active and inactive TR agonists using radial distribution function (RDF) descriptors as predictor variables. The classifications made by both LDA and LR models on the training set compounds resulted in an accuracy of 84.3%, sensitivity of 72.2% and specificity of 90.9%. The areas under the ROC curves, constructed with the training set data, were found to be 0.872 and 0.880 for LDA and LR models, respectively. External validation of the models revealed that 76.5% of the test set compounds were correctly classified by both LDA and LR models. These findings suggest that the two models reported in this paper are good and reliable for classifying OH-PCB congeners into active and inactive TR agonists.
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Affiliation(s)
- L K Akinola
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
- Department of Chemistry, Bauchi State University, Gadau, Nigeria
| | - A Uzairu
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
| | - G A Shallangwa
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
| | - S E Abechi
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
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4
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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Saini R, Fatima S, Agarwal SM. TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors. Chem Biol Drug Des 2021; 96:921-930. [PMID: 33058464 DOI: 10.1111/cbdd.13697] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 12/26/2022]
Abstract
The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance-based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross-validation and independent validation datasets. It was observed that the random forest-based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open-source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti-TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.
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Affiliation(s)
- Ravi Saini
- School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, India
| | - Shehnaz Fatima
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, Noida, India
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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7
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Mondal D, Ghosh K, Baidya ATK, Gantait AM, Gayen S. Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics. Toxicol Mech Methods 2020; 30:257-265. [DOI: 10.1080/15376516.2019.1709238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Dipayan Mondal
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Kalyan Ghosh
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Anurag T. K. Baidya
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | | | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
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8
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Wang J, Ran T, Chen Y, Lu T. Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors. Chem Biol Drug Des 2019; 96:1114-1122. [PMID: 31855311 DOI: 10.1111/cbdd.13656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 11/23/2019] [Accepted: 12/07/2019] [Indexed: 11/27/2022]
Abstract
Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune diseases. In this work, we constructed a Bayesian classification model for virtually seeking novel BTK inhibitors, which showed good performance in terms of screening efficiency and accuracy. Through searching for several chemical libraries including Chembl_17 (1,317,484 compounds), Chembridge (103,473 compounds), and Chemdiv (700,000 compounds) using this model followed by molecular docking and activity prediction, 52 compounds with novel scaffolds were acknowledged as potential BTK inhibitors, which could be promising starting points for further exploration. This study also provided a guide to construct an efficient and effective protocol for virtual screening by integrating machine learning methods.
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Affiliation(s)
- Jian Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.,Zhejiang Pharmaceutical College, Ningbo, China
| | - Ting Ran
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Yadong Chen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Tao Lu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
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9
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Zhou WN, Zhang YM, Qiao X, Pan J, Yin LF, Zhu L, Zhao JN, Lu S, Lu T, Chen YD, Liu HC. Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors. Curr Comput Aided Drug Des 2019; 15:193-205. [PMID: 30411690 DOI: 10.2174/1573409914666181109110030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 08/11/2018] [Accepted: 10/16/2018] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. METHODS In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. RESULTS Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. CONCLUSION In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.
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Affiliation(s)
- Wei-Neng Zhou
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Yan-Min Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Xin Qiao
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Jing Pan
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Ling-Feng Yin
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Lu Zhu
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Jun-Nan Zhao
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Shuai Lu
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Ya-Dong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Hai-Chun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China
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2-Pyrazol-1-yl-thiazole derivatives as novel highly potent antibacterials. J Antibiot (Tokyo) 2019; 72:827-833. [DOI: 10.1038/s41429-019-0211-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 04/23/2019] [Accepted: 06/16/2019] [Indexed: 02/06/2023]
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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Yang S, Shen Y, Lu W, Yang Y, Wang H, Li L, Wu C, Du G. Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription. BIOMED RESEARCH INTERNATIONAL 2019; 2019:6847685. [PMID: 31360720 PMCID: PMC6652039 DOI: 10.1155/2019/6847685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 05/13/2019] [Accepted: 05/26/2019] [Indexed: 12/18/2022]
Abstract
Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.
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Affiliation(s)
- Shilun Yang
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Yanjia Shen
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Wendan Lu
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Yinglin Yang
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Haigang Wang
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Li Li
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
| | - Chunfu Wu
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
| | - Guanhua Du
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103, Wen hua Road, Shenyang 110016, China
- Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 2, Nan wei Road, Beijing 100050, China
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13
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Jain S, Bhardwaj B, Amin SA, Adhikari N, Jha T, Gayen S. Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling. J Biomol Struct Dyn 2019; 38:1683-1696. [PMID: 31057090 DOI: 10.1080/07391102.2019.1615000] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sanskar Jain
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Bhagwati Bhardwaj
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Sk Abdul Amin
- Natural Science Laboratory, Department of Pharmaceutical Technology, Division of Medicinal and Pharmaceutical Chemistry, Jadavpur University, Kolkata, West Bengal, India
| | - Nilanjan Adhikari
- Natural Science Laboratory, Department of Pharmaceutical Technology, Division of Medicinal and Pharmaceutical Chemistry, Jadavpur University, Kolkata, West Bengal, India
| | - Tarun Jha
- Natural Science Laboratory, Department of Pharmaceutical Technology, Division of Medicinal and Pharmaceutical Chemistry, Jadavpur University, Kolkata, West Bengal, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
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14
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Kawczak P, Bober L, Bączek T. Activity Evaluation and Selection of Some Classes of Antibiotics with the use of Semi-Empirical Quantum Mechanics and Quantitative Structure- Activity Relationships Approach. Comb Chem High Throughput Screen 2019; 22:97-112. [PMID: 31020936 DOI: 10.2174/1386207322666190425144209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 03/28/2019] [Accepted: 04/12/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND A set of β-lactam antibiotics, aminoglycoside antibiotics, and tetracycline antibiotics were proposed and analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. OBJECTIVE The characterization of selected antimicrobial compounds in terms of both physicochemical and pharmacological on the basis of calculations of quantum mechanics and possessed biological activity data. METHODS During the study, Multiple Linear Regression (MLR) supported with Factor Analysis (FA) and Principal Component Analysis (PCA) was made, as the types of proposed chemometric approach; the semi-empirical level of in silico molecular modeling was used for calculations and comparison of molecular descriptors both in a vacuum and in the aquatic environment. RESULTS The relationships between structure and microbiological activity enabled the characterization and description of the analyzed molecules using statistically significant descriptors belonging in most cases to different structural, geometric and electronic elements defining at the same time the properties of the studied three different classes of examined antibiotics. CONCLUSION The chemometric methods used revealed the influence of some of the elements of structures examined molecules belonging to main antibiotics classes and responsible for the antimicrobial activity.
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Affiliation(s)
- Piotr Kawczak
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy with Subfaculty of Laboratory Medicine, Medical University of Gdansk, Gdansk 80-416, Poland
| | - Leszek Bober
- Polpharma SA Pharmaceutical Works, Starogard Gdanski 83-200, Poland
| | - Tomasz Bączek
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy with Subfaculty of Laboratory Medicine, Medical University of Gdansk, Gdansk 80-416, Poland.,Department of Nursing, Faculty of Health Sciences, Pomeranian University of Słupsk, Słupsk 76-200, Poland
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15
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Pecoraro B, Tutone M, Hoffman E, Hutter V, Almerico AM, Traynor M. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies. J Chem Inf Model 2019; 59:1759-1771. [PMID: 30658035 DOI: 10.1021/acs.jcim.8b00934] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.
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Affiliation(s)
- Beatrice Pecoraro
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Marco Tutone
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Ewelina Hoffman
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Victoria Hutter
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Anna Maria Almerico
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Matthew Traynor
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
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16
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Yu X, Yang H, Huang X. Novel Method for Structure-Activity Relationship of Aptamer Sequences for Human Prostate Cancer. ACS OMEGA 2018; 3:10002-10007. [PMID: 31459128 PMCID: PMC6644987 DOI: 10.1021/acsomega.8b01464] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 08/20/2018] [Indexed: 06/10/2023]
Abstract
Prostate cancer (PCa) is one of the most common malignancies in men and seriously threatens men's health. Developing aptamer probes for PCa cells is of great significance for early diagnosis and treatment of PCa. This paper reports a classification model for SELEX-based aptamers, which were obtained with PCa cell line PCa-3M-1E8 (highly metastatic tumor cell) as target cells and PCa cell line PCa-3M-2B4 (low metastatic tumor cell) as control cells. On the basis of the SELEX principle, 100 oligonucleotide sequences from the 3rd round of SELEX were defined as low affinity and specificity aptamers, and 100 sequences from the 11th round were set as high affinity and specificity aptamers. Seven molecular descriptors were used for the classification model, which were calculated from amino acid sequences translated from DNA aptamer sequences with DNAMAN software. The classification model based on binary logical regression analysis has prediction accuracies, sensitivity, and specificity of about 80% for both the training set and test set. Therefore, it is feasible to calculate molecular descriptors from amino acid sequence translated from DNA aptamer sequences and develop a classification model for PCa cell line PCa-3M-1E8.
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Affiliation(s)
- Xinliang Yu
- College
of Chemistry and Chemical Engineering, Hunan
Institute of Engineering, Xiangtan, Hunan 411104, China
- State
Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha, Hunan 410082, China
| | - Huiqiong Yang
- College
of Chemistry and Chemical Engineering, Hunan
Institute of Engineering, Xiangtan, Hunan 411104, China
| | - Xianwei Huang
- College
of Chemistry and Chemical Engineering, Hunan
Institute of Engineering, Xiangtan, Hunan 411104, China
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17
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QSAR-based molecular signatures of prenylated (iso)flavonoids underlying antimicrobial potency against and membrane-disruption in Gram positive and Gram negative bacteria. Sci Rep 2018; 8:9267. [PMID: 29915354 PMCID: PMC6006161 DOI: 10.1038/s41598-018-27545-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 06/05/2018] [Indexed: 12/27/2022] Open
Abstract
Prenylated flavonoids and isoflavonoids are phytochemicals with remarkable antibacterial activity. In this study, 30 prenylated (iso)flavonoids were tested against Listeria monocytogenes and Escherichia coli (the latter in combination with an efflux pump inhibitor). Minimum inhibitory concentrations of the most active compounds ranged between 6.3–15.0 µg/mL. Quantitative structure-activity relationships (QSAR) analysis was performed and linear regression models were proposed with R2 between 0.77–0.80, average R2m between 0.70–0.75, Q2LOO between 0.66–0.69, and relatively low amount of descriptors. Shape descriptors (related to flexibility and globularity), together with hydrophilic/hydrophobic volume and surface area descriptors, were identified as important molecular characteristics related to activity. A 3D pharmacophore model explaining the effect of the prenyl position on the activity of compounds was developed for each bacterium. These models predicted active compounds with an accuracy of 71–88%. With regard to the mode of action, good antibacterial prenylated (iso)flavonoids with low relative hydrophobic surface area caused remarkable membrane permeabilization, whereas those with higher relative hydrophobic surface area did not. Based on the QSAR and membrane permeabilization studies, the mode of action of antibacterial prenylated (iso)flavonoids was putatively rationalized.
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18
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Danishuddin, Kumar A, Mobeen F, Khan AU. Development of Ligand and Structure-based classification models to design novel inhibitors against antibiotic hydrolyzing enzymes: Integration of web server. J Biomol Struct Dyn 2017; 36:2966-2975. [PMID: 28849700 DOI: 10.1080/07391102.2017.1373034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Danishuddin
- a Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit , Aligarh Muslim University , Aligarh , UP 202002 , India
| | - Amit Kumar
- a Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit , Aligarh Muslim University , Aligarh , UP 202002 , India
| | - Fauzul Mobeen
- a Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit , Aligarh Muslim University , Aligarh , UP 202002 , India
| | - Asad U Khan
- a Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit , Aligarh Muslim University , Aligarh , UP 202002 , India
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19
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Structural exploration of PPARγ modulators through pharmacophore mapping, fragment-based design, docking, and molecular dynamics simulation analyses. Med Chem Res 2016. [DOI: 10.1007/s00044-016-1727-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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20
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AbdulHameed MDM, Ippolito DL, Wallqvist A. Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chem Res Toxicol 2016; 29:1729-1740. [DOI: 10.1021/acs.chemrestox.6b00227] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, Maryland 21702, United States
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort
Detrick, Maryland 21702, United States
| | - Anders Wallqvist
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, Maryland 21702, United States
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21
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Raevsky OA, Polianczyk DE, Mukhametov A, Grigorev VY. Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:629-635. [PMID: 27477321 DOI: 10.1080/1062936x.2016.1212922] [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: 04/29/2016] [Accepted: 07/11/2016] [Indexed: 06/06/2023]
Abstract
Assessment of "CNS drugs/CNS candidates" classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds. Aggregation of all six properties in the MPOscore did not improve the classification, which was worse than the classification using only MW. The results of our study demonstrate the imperfection of the CNS MPO approach; in its current form it is not very useful for computer design of new, effective CNS drugs.
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Affiliation(s)
- O A Raevsky
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - D E Polianczyk
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - A Mukhametov
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
| | - V Y Grigorev
- a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation
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22
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Raevsky OA, Grigorev VY, Polianczyk DE, Sandakov GI, Solodova SL, Yarkov AV, Bachurin SO, Dearden JC. Physicochemical property profile for brain permeability: comparative study by different approaches. J Drug Target 2016; 24:655-62. [DOI: 10.3109/1061186x.2015.1132224] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Oleg A. Raevsky
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - Veniamin Y. Grigorev
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - Daniel E. Polianczyk
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - German I. Sandakov
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - Svetlana L. Solodova
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - Alexander V. Yarkov
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - Sergey O. Bachurin
- Department of Biomedicinal Chemistry, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russia
| | - John C. Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
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23
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Raevsky OA. CNS Multiparameter Optimization Approach: Is it in Accordance with Occam’s Razor Principle? Mol Inform 2016; 35:94-8. [DOI: 10.1002/minf.201500109] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 12/11/2015] [Indexed: 01/30/2023]
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24
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Fang J, Pang X, Yan R, Lian W, Li C, Wang Q, Liu AL, Du GH. Discovery of neuroprotective compounds by machine learning approaches. RSC Adv 2016. [DOI: 10.1039/c5ra23035g] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The classification models were constructed to discover neuroprotective compounds against glutamate or H2O2-induced neurotoxicity through machine learning approaches.
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Affiliation(s)
- Jiansong Fang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
- Institute of Clinical Pharmacology
| | - Xiaocong Pang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
| | - Rong Yan
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
| | - Wenwen Lian
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
| | - Chao Li
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
| | - Qi Wang
- Institute of Clinical Pharmacology
- Guangzhou University of Traditional Chinese Medicine
- Guangzhou 510006
- China
| | - Ai-Lin Liu
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
- Beijing Key Laboratory of Drug Target and Screening Research
| | - Guan-Hua Du
- Institute of Materia Medica
- Chinese Academy of Medical Sciences and Peking Union Medical College
- Beijing 100050
- PR China
- Beijing Key Laboratory of Drug Target and Screening Research
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25
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Xiong X, Yuan H, Zhang Y, Xu J, Ran T, Liu H, Lu S, Xu A, Li H, Jiang Y, Lu T, Chen Y. Protein flexibility oriented virtual screening strategy for JAK2 inhibitors. J Mol Struct 2015. [DOI: 10.1016/j.molstruc.2015.05.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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26
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Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. J Chem Inf Model 2015; 55:1246-60. [PMID: 25995041 DOI: 10.1021/acs.jcim.5b00144] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.
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Affiliation(s)
- Alex M Clark
- †Molecular Materials Informatics, Inc., 1900 St. Jacques No. 302, Montreal H3J 2S1, Quebec, Canada
| | - Sean Ekins
- ‡Collaborations Pharmaceuticals, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,§Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,∥Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
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27
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Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds. Mol Divers 2015; 19:895-913. [DOI: 10.1007/s11030-015-9592-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 03/25/2015] [Indexed: 12/24/2022]
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28
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Fang J, Yang R, Gao L, Yang S, Pang X, Li C, He Y, Liu AL, Du GH. Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery. Mol Divers 2014; 19:149-62. [DOI: 10.1007/s11030-014-9561-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2014] [Accepted: 11/25/2014] [Indexed: 02/07/2023]
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29
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Liu Z, Zheng M, Yan X, Gu Q, Gasteiger J, Tijhuis J, Maas P, Li J, Xu J. ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability. J Comput Aided Mol Des 2014; 28:941-50. [PMID: 25031075 DOI: 10.1007/s10822-014-9778-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2014] [Accepted: 07/09/2014] [Indexed: 11/26/2022]
Abstract
Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.
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Affiliation(s)
- Zhihong Liu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, 132 East Circle at University City, Guangzhou, 510006, China
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30
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Kandel DD, Raychaudhury C, Pal D. Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity. J Mol Model 2014; 20:2164. [PMID: 24664120 DOI: 10.1007/s00894-014-2164-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Accepted: 01/30/2014] [Indexed: 11/26/2022]
Abstract
Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices)--one based solely on topological considerations without discriminating atom or bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.
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31
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Ekins S, Freundlich JS, Hobrath JV, Lucile White E, Reynolds RC. Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery. Pharm Res 2013; 31:414-35. [PMID: 24132686 DOI: 10.1007/s11095-013-1172-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2013] [Accepted: 07/28/2013] [Indexed: 12/19/2022]
Abstract
PURPOSE Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. METHODS A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. RESULTS In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. CONCLUSION Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA,
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Poongavanam V, Kongsted J. Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition. PLoS One 2013; 8:e73478. [PMID: 24066050 PMCID: PMC3774690 DOI: 10.1371/journal.pone.0073478] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Accepted: 07/19/2013] [Indexed: 11/28/2022] Open
Abstract
The increasing resistance to current therapeutic agents for HIV drug regiment remains a major problem for effective acquired immune deficiency syndrome (AIDS) therapy. Many potential inhibitors have today been developed which inhibits key cellular pathways in the HIV cycle. Inhibition of HIV-1 reverse transcriptase associated ribonuclease H (RNase H) function provides a novel target for anti-HIV chemotherapy. Here we report on the applicability of conceptually different in silico approaches as virtual screening (VS) tools in order to efficiently identify RNase H inhibitors from large chemical databases. The methods used here include machine-learning algorithms (e.g. support vector machine, random forest and kappa nearest neighbor), shape similarity (rapid overlay of chemical structures), pharmacophore, molecular interaction fields-based fingerprints for ligands and protein (FLAP) and flexible ligand docking methods. The results show that receptor-based flexible docking experiments provides good enrichment (80–90%) compared to ligand-based approaches such as FLAP (74%), shape similarity (75%) and random forest (72%). Thus, this study suggests that flexible docking experiments is the model of choice in terms of best retrieval of active from inactive compounds and efficiency and efficacy schemes. Moreover, shape similarity, machine learning and FLAP models could also be used for further validation or filtration in virtual screening processes. The best models could potentially be use for identifying structurally diverse and selective RNase H inhibitors from large chemical databases. In addition, pharmacophore models suggest that the inter-distance between hydrogen bond acceptors play a key role in inhibition of the RNase H domain through metal chelation.
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Affiliation(s)
| | - Jacob Kongsted
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
- * E-mail:
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Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models. PLoS One 2013; 8:e63240. [PMID: 23667592 PMCID: PMC3647004 DOI: 10.1371/journal.pone.0063240] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2012] [Accepted: 03/31/2013] [Indexed: 02/01/2023] Open
Abstract
High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
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