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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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2
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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: 7] [Impact Index Per Article: 3.5] [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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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: 95] [Impact Index Per Article: 31.7] [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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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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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: 342] [Impact Index Per Article: 68.4] [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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Bellera CL, Talevi A. Quantitative structure-activity relationship models for compounds with anticonvulsant activity. Expert Opin Drug Discov 2019; 14:653-665. [PMID: 31072145 DOI: 10.1080/17460441.2019.1613368] [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: 01/18/2023]
Abstract
Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.
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Affiliation(s)
- Carolina L Bellera
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
| | - Alan Talevi
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
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Bera I, Marathe MV, Payghan PV, Ghoshal N. Identification of novel hits as highly prospective dual agonists for mu and kappa opioid receptors: an integrated in silico approach. J Biomol Struct Dyn 2017; 36:279-301. [DOI: 10.1080/07391102.2016.1275810] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Indrani Bera
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology , Kolkata 700032, India
| | | | - Pavan V. Payghan
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology , Kolkata 700032, India
| | - Nanda Ghoshal
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology , Kolkata 700032, India
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8
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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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9
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Vijayan RSK, Trivedi N, Roy SN, Bera I, Manoharan P, Payghan PV, Bhattacharyya D, Ghoshal N. Modeling the Closed and Open State Conformations of the GABAA Ion Channel - Plausible Structural Insights for Channel Gating. J Chem Inf Model 2012; 52:2958-69. [DOI: 10.1021/ci300189a] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- R. S. K. Vijayan
- Structural Biology and Bioinformatics
Division, CSIR - Indian Institute of Chemical Biology, Kolkata −700 032, India
| | - Neha Trivedi
- National Institute of Pharmaceutical Education and Research, Kolkata −700
032, India
| | - Sudipendra Nath Roy
- National Institute of Pharmaceutical Education and Research, Kolkata −700
032, India
| | - Indrani Bera
- Structural Biology and Bioinformatics
Division, CSIR - Indian Institute of Chemical Biology, Kolkata −700 032, India
| | - Prabu Manoharan
- Structural Biology and Bioinformatics
Division, CSIR - Indian Institute of Chemical Biology, Kolkata −700 032, India
| | - Pavan V. Payghan
- Structural Biology and Bioinformatics
Division, CSIR - Indian Institute of Chemical Biology, Kolkata −700 032, India
| | | | - Nanda Ghoshal
- Structural Biology and Bioinformatics
Division, CSIR - Indian Institute of Chemical Biology, Kolkata −700 032, India
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Singh N, Chaudhury S, Liu R, AbdulHameed MDM, Tawa G, Wallqvist A. QSAR Classification Model for Antibacterial Compounds and Its Use in Virtual Screening. J Chem Inf Model 2012; 52:2559-69. [PMID: 23013546 DOI: 10.1021/ci300336v] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Narender Singh
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
| | - Sidhartha Chaudhury
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
| | - Ruifeng Liu
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
| | - Mohamed Diwan M. AbdulHameed
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
| | - Gregory Tawa
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
| | - Anders Wallqvist
- DoD Biotechnology High Performance Software Applications Institute, BHSAI/MRMC, ATTN: MCMR-TT, 2405
Whittier Drive, Frederick, Maryland 21702, United States
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Ghoshal N, Vijayan RSK. Pharmacophore models for GABA(A) modulators: implications in CNS drug discovery. Expert Opin Drug Discov 2012; 5:441-60. [PMID: 22823129 DOI: 10.1517/17460441003789363] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD GABA(A) ion channel is a validated drug target, implicated in the pathophysiology of various neurological and psychiatric disorders. Structural investigations on GABA(A) are currently precluded in the absence of experimentally resolved structure. Pharmacophore modeling circumvents such issues and proves to be a powerful and successful method in drug discovery. AREAS COVERED IN THIS REVIEW The present reviews encompass pharmacophoric models available in the literature for the orthosteric GABA and the allosteric benzodiazepine binding site. Success stories from these simplistic pharmacophore models in scaffold hopping and strategic lead optimization have been highlighted. Recent advances in pharmacophore modeling that can leverage CNS drug discovery programs and deliver astounding results have been reviewed. WHAT THE READER WILL GAIN Readers are bound to gain a comprehensive insight on different computational techniques used by different groups to arrive at simple, yet sophisticated pharmacophore models. In the absence of experimentally unresolved active site geometry of GABA(A), these models will provide the reader an opportunity to translate these pharmacophoric features to the microscopic phenomenon of supramolecular ligand interaction. TAKE HOME MESSAGE Pharmacophore modeling has now evolved as a mainstay approach for lead generation and optimization in drug discovery programs. Of late, many advances in pharmacophore perception have emerged. Such advancements should be used to confront activity profiling and early stage risk assessment in a high-throughput fashion. Extending such technologies has the potential not only to reduce time and cost, but also to prevent late stage attrition in drug discovery.
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Affiliation(s)
- Nanda Ghoshal
- Indian Institute of Chemical Biology (A unit of CSIR), Structural Biology and Bioinformatics Division, 4, Raja S.C. Mullick Road, Kolkata-700032, India +91 33 2473 3491 ext. 854 ; +91 33 2473 5197 ;
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Choi J, He N, Kim N, Yoon S. Enrichment of virtual hits by progressive shape-matching and docking. J Mol Graph Model 2011; 32:82-8. [PMID: 22088763 DOI: 10.1016/j.jmgm.2011.10.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 10/01/2011] [Accepted: 10/03/2011] [Indexed: 11/17/2022]
Abstract
The main applications of virtual chemical screening include the selection of a minimal receptor-relevant subset of a chemical library with a maximal chemical diversity. We have previously reported that the combination of ligand-centric and receptor-centric virtual screening methods may provide a compromise between computational time and accuracy during the hit enrichment process. In the present work, we propose a "progressive distributed docking" method that improves the virtual screening process using an iterative combination of shape-matching and docking steps. Known ligands with low docking scores were used as initial 3D templates for the shape comparisons with the chemical library. Next, new compounds with good template shape matches and low receptor docking scores were selected for the next round of shape searching and docking. The present iterative virtual screening process was tested for enriching peroxisome proliferator-activated receptor and phosphoinositide 3-kinase relevant compounds from a selected subset of the chemical libraries. It was demonstrated that the iterative combination improved the lead-hopping practice by improving the chemical diversity in the selected list of virtual hits.
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Affiliation(s)
- Jiwon Choi
- Department of Biological Sciences, Research Center for Women's Diseases, Sookmyung Women's University, Hyochangwongil 52, Yongsan-gu, Seoul 140-742, Republic of Korea
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Maganti L, Das SK, Mascarenhas NM, Ghoshal N. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits. Mol Inform 2011; 30:863-72. [DOI: 10.1002/minf.201100056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2011] [Accepted: 07/05/2011] [Indexed: 11/06/2022]
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Zhu R, Liu Q, Tang J, Li H, Cao Z. Investigations on inhibitors of hedgehog signal pathway: a quantitative structure-activity relationship study. Int J Mol Sci 2011; 12:3018-33. [PMID: 21686166 PMCID: PMC3116172 DOI: 10.3390/ijms12053018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2011] [Revised: 04/20/2011] [Accepted: 04/28/2011] [Indexed: 12/30/2022] Open
Abstract
The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.
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Affiliation(s)
- Ruixin Zhu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China; E-Mails: (R.Z.); (Q.L.)
| | - Qi Liu
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China; E-Mails: (R.Z.); (Q.L.)
| | - Jian Tang
- Department of Natural Medicinal Chemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China; E-Mail:
| | - Huiliang Li
- Department of Natural Medicinal Chemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China; E-Mail:
| | - Zhiwei Cao
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China; E-Mails: (R.Z.); (Q.L.)
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González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276:229-49. [DOI: 10.1016/j.jtbi.2011.01.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 12/02/2010] [Accepted: 01/10/2011] [Indexed: 10/18/2022]
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16
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Das S, Krein MP, Breneman CM. Binding affinity prediction with property-encoded shape distribution signatures. J Chem Inf Model 2010; 50:298-308. [PMID: 20095526 PMCID: PMC2846646 DOI: 10.1021/ci9004139] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.
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Affiliation(s)
- Sourav Das
- Department of Chemistry & Chemical Biology, Rensselaer Polytechnic Institute, 110-8th Street, Troy, NY 12180
| | - Michael P. Krein
- Department of Chemistry & Chemical Biology, Rensselaer Polytechnic Institute, 110-8th Street, Troy, NY 12180
| | - Curt M. Breneman
- Department of Chemistry & Chemical Biology / RECCR Center Rensselaer Polytechnic Institute, 110-8th Street, Center for Biotechnology and Interdisciplinary Studies, Troy, NY 12180, Phone Number: 518-276-2678, Fax Number: 518-276-4887,
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