1
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Kazakova E, Lane TR, Jones T, Puhl AC, Riabova O, Makarov V, Ekins S. 1-Sulfonyl-3-amino-1 H-1,2,4-triazoles as Yellow Fever Virus Inhibitors: Synthesis and Structure-Activity Relationship. ACS OMEGA 2023; 8:42951-42965. [PMID: 38024733 PMCID: PMC10653066 DOI: 10.1021/acsomega.3c06106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/10/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023]
Abstract
Yellow fever virus (YFV) transmitted by infected mosquitoes causes an acute viral disease for which there are no approved small-molecule therapeutics. Our recently developed machine learning models for YFV inhibitors led to the selection of a new pyrazolesulfonamide derivative RCB16003 with acceptable in vitro activity. We report that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class, which was recently identified as active non-nucleoside reverse transcriptase inhibitors against HIV-1, can also be repositioned as inhibitors of yellow fever virus replication. As compared to other Flaviviridae or Togaviridae family viruses tested, both compounds RCB16003 and RCB16007 demonstrate selectivity for YFV over related viruses, with only RCB16007 showing some inhibition of the West Nile virus (EC50 7.9 μM, CC50 17 μM, SI 2.2). We also describe the absorption, distribution, metabolism, and excretion (ADME) in vitro and pharmacokinetics (PK) for RCB16007 in mice. This compound had previously been shown to not inhibit hERG, and we now describe that it has good metabolic stability in mouse and human liver microsomes, low levels of CYP inhibition, high protein binding, and no indication of efflux in Caco-2 cells. A single-dose oral PK study in mice has a T1/2 of 3.4 h and Cmax of 1190 ng/mL, suggesting good availability and stability. We now propose that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class may be prioritized for in vivo efficacy testing against YFV.
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Affiliation(s)
- Elena Kazakova
- Federal
Research Centre “Fundamentals of Biotechnology” of the
Russian Academy of Sciences (Research Centre of Biotechnology RAS), 33-2 Leninsky Prospect, 119071 Moscow, Russia
| | - Thomas R. Lane
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thane Jones
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Ana C. Puhl
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Olga Riabova
- Federal
Research Centre “Fundamentals of Biotechnology” of the
Russian Academy of Sciences (Research Centre of Biotechnology RAS), 33-2 Leninsky Prospect, 119071 Moscow, Russia
| | - Vadim Makarov
- Federal
Research Centre “Fundamentals of Biotechnology” of the
Russian Academy of Sciences (Research Centre of Biotechnology RAS), 33-2 Leninsky Prospect, 119071 Moscow, Russia
| | - Sean Ekins
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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2
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Rodríguez-Pérez R, Bajorath J. Explainable Machine Learning for Property Predictions in Compound Optimization. J Med Chem 2021; 64:17744-17752. [PMID: 34902252 DOI: 10.1021/acs.jmedchem.1c01789] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The prediction of compound properties from chemical structure is a main task for machine learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications such as compound screening, virtual library enumeration, or generative chemistry. Albeit desirable, a detailed understanding of ML model decisions is typically not required in these cases. By contrast, compound optimization efforts rely on small data sets to identify structural modifications leading to desired property profiles. In this situation, if ML is applied, one usually is reluctant to make decisions based on predictions that cannot be rationalized. Only few ML methods are interpretable. However, to yield insights into complex ML model decisions, explanatory approaches can be applied. Herein, methodologies for better understanding of ML models or explaining individual predictions are reviewed and current challenges in integrating ML into medicinal chemistry programs as well as future opportunities are discussed.
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Affiliation(s)
- Raquel Rodríguez-Pérez
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany.,Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany
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3
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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4
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Gawriljuk VO, Foil DH, Puhl AC, Zorn KM, Lane TR, Riabova O, Makarov V, Godoy AS, Oliva G, Ekins S. Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus. J Chem Inf Model 2021; 61:3804-3813. [PMID: 34286575 DOI: 10.1021/acs.jcim.1c00460] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 μM and CC50 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.
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Affiliation(s)
- Victor O Gawriljuk
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100 - Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Daniel H Foil
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Ana C Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Thomas R Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Olga Riabova
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071 Moscow, Russia
| | - Vadim Makarov
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071 Moscow, Russia
| | - Andre S Godoy
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100 - Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Glaucius Oliva
- São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100 - Santa Angelina, São Carlos, São Paulo 13563-120, Brazil
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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5
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Krishna S, Lakra AD, Shukla N, Khan S, Mishra DP, Ahmed S, Siddiqi MI. Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation. J Biomol Struct Dyn 2019; 38:3280-3295. [DOI: 10.1080/07391102.2019.1654925] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Shagun Krishna
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Amar Deep Lakra
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Nidhi Shukla
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Saman Khan
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Durga Prasad Mishra
- Endocrinology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Shakil Ahmed
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Mohammad Imran Siddiqi
- Molecular & Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
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6
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Maltarollo VG, Kronenberger T, Espinoza GZ, Oliveira PR, Honorio KM. Advances with support vector machines for novel drug discovery. Expert Opin Drug Discov 2018; 14:23-33. [PMID: 30488731 DOI: 10.1080/17460441.2019.1549033] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.
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Affiliation(s)
- Vinicius Gonçalves Maltarollo
- a Departamento de Produtos Farmacêuticos, Faculdade de Farmácia , Universidade Federal de Minas Gerais , Belo Horizonte , Brazil
| | - Thales Kronenberger
- b Department of Internal Medicine VIII , University Hospital of Tübingen , Tübingen , Germany
| | - Gabriel Zarzana Espinoza
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil
| | - Patricia Rufino Oliveira
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil
| | - Kathia Maria Honorio
- c Escola de Artes, Ciências e Humanidades , Universidade de São Paulo (USP) , São Paulo , Brazil.,d Centro de Ciências Naturais e Humanas , Universidade Federal do ABC , Santo André , Brazil
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7
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Polishchuk P. Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future. J Chem Inf Model 2017; 57:2618-2639. [DOI: 10.1021/acs.jcim.7b00274] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and
Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
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8
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Axen SD, Huang XP, Cáceres EL, Gendelev L, Roth BL, Keiser MJ. A Simple Representation of Three-Dimensional Molecular Structure. J Med Chem 2017; 60:7393-7409. [PMID: 28731335 DOI: 10.1021/acs.jmedchem.7b00696] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the extended connectivity fingerprint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the extended three-dimensional fingerprint (E3FP). By integrating E3FP with the similarity ensemble approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442-0.637 kcal/mol/heavy atom.
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Affiliation(s)
- Seth D Axen
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States
| | - Xi-Ping Huang
- Department of Pharmacology, University of North Carolina School of Medicine , Chapel Hill, North Carolina 27599, United States.,National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Elena L Cáceres
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States.,Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States
| | - Leo Gendelev
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States.,Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina School of Medicine , Chapel Hill, North Carolina 27599, United States.,National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), University of North Carolina , Chapel Hill, North Carolina 27599, United States.,Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599, United States
| | - Michael J Keiser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States.,Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, and Institute for Computational Health Sciences, University of California, San Francisco , 675 Nelson Rising Lane NS 416A, San Francisco, California 94143, United States
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9
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Dai SX, Li GH, Gao YD, Huang JF. Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs. Mol Inform 2015; 35:81-91. [PMID: 27491793 DOI: 10.1002/minf.201500075] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 09/21/2015] [Indexed: 01/04/2023]
Abstract
GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.
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Affiliation(s)
- Shao-Xing Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, P. R. China phone/fax: + 86 087165199200/+ 86 087165199200.,Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, P. R. China phone/fax: + 86 087165199200/+ 86 087165199200.,Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yue-Dong Gao
- Kunming Biological Diversity Regional Center of Instruments, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, P. R. China
| | - Jing-Fei Huang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, P. R. China phone/fax: + 86 087165199200/+ 86 087165199200. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, P. R. China. .,Kunming Institute of Zoology - Chinese University of Hongkong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming 650223, P. R. China.
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10
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Balfer J, Bajorath J. Visualization and Interpretation of Support Vector Machine Activity Predictions. J Chem Inf Model 2015; 55:1136-47. [DOI: 10.1021/acs.jcim.5b00175] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Jenny Balfer
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
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11
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Reker D, Schneider G. Active-learning strategies in computer-assisted drug discovery. Drug Discov Today 2015; 20:458-65. [DOI: 10.1016/j.drudis.2014.12.004] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 11/13/2014] [Accepted: 12/02/2014] [Indexed: 12/20/2022]
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12
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Carvalho LCR, Ribeiro D, Seixas RSGR, Silva AMS, Nave M, Martins AC, Erhardt S, Fernandes E, Cabrita EJ, Marques MMB. Synthesis and evaluation of new benzimidazole-based COX inhibitors: a naproxen-like interaction detected by STD-NMR. RSC Adv 2015. [DOI: 10.1039/c5ra04984a] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Non-steroidal anti-inflammatory drugs exert their pharmacological activity through inhibition of cyclooxygenase 1 and 2 (COX-1 and COX-2).
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Affiliation(s)
- Luísa C. R. Carvalho
- LAQV@REQUIMTE
- Departamento de Química
- Faculdade de Ciências e Tecnologia
- Universidade Nova de Lisboa
- Campus de Caparica
| | - Daniela Ribeiro
- UCIBIO@REQUIMTE
- Departamento de Ciências Químicas
- Laboratório de Química Aplicada
- Faculdade de Farmácia
- Universidade do Porto
| | | | - Artur M. S. Silva
- QOPNA & Departamento de Química
- Universidade de Aveiro
- 3810-193 Aveiro
- Portugal
| | - Mariana Nave
- LAQV@REQUIMTE
- Departamento de Química
- Faculdade de Ciências e Tecnologia
- Universidade Nova de Lisboa
- Campus de Caparica
| | - Ana C. Martins
- LAQV@REQUIMTE
- Departamento de Química
- Faculdade de Ciências e Tecnologia
- Universidade Nova de Lisboa
- Campus de Caparica
| | - Stefan Erhardt
- School of Life
- Sport and Social Sciences
- Edinburgh Napier University
- Edinburgh EH11 4BN
- UK
| | - Eduarda Fernandes
- UCIBIO@REQUIMTE
- Departamento de Ciências Químicas
- Laboratório de Química Aplicada
- Faculdade de Farmácia
- Universidade do Porto
| | - Eurico J. Cabrita
- UCIBIO@REQUIMTE
- Departamento de Química
- Faculdade de Ciências e Tecnologia
- Universidade Nova de Lisboa
- 2829-516 Caparica
| | - M. Manuel B. Marques
- LAQV@REQUIMTE
- Departamento de Química
- Faculdade de Ciências e Tecnologia
- Universidade Nova de Lisboa
- Campus de Caparica
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13
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Rathore A, Rahman MU, Siddiqui AA, Ali A, Shaharyar M. Design and synthesis of benzimidazole analogs endowed with oxadiazole as selective COX-2 inhibitor. Arch Pharm (Weinheim) 2014; 347:923-35. [PMID: 25303727 DOI: 10.1002/ardp.201400219] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Revised: 07/28/2014] [Accepted: 08/15/2014] [Indexed: 01/30/2023]
Abstract
New molecules of benzimidazole endowed with oxadiazole were designed and synthesized from 2-(2-((pyrimidin-2-ylthio)methyl)-1H-benzo[d]imidazol-1-yl)acetohydrazide as 1-((5-substituted alkyl/aryl-1,3,4-oxadiazol-2-yl)methyl)-2-((pyrimidin-2-ylthio)methyl)-1H-benzimidazoles (5a-r) with the aim to acquire selective cyclooxygenase (COX-2) inhibitor activity. The synthesized compounds were screened by in vitro cyclooxygenase assays to determine COX-1 and COX-2 inhibitory potency and the results showed that they had good-to-remarkable activity with an IC50 range of 11.6-56.1 µM. The most active compounds were further screened for their in vivo anti-inflammatory activity by using the carrageenan-induced rat paw edema model. In vitro anticancer activities of the hybrid compounds were assessed by the National Cancer Institute (NCI), USA, against 60 human cell lines, and the results showed a good spectrum. Compound 5l exhibited significant COX-2 inhibition with an IC50 value of 8.2 µM and a percent protection of 68.4%. Compound 5b evinced moderate cytotoxicity toward the UO-31 cell line of renal cancer. A docking study was performed using Maestro 9.0, to provide the binding mode into the binding sites of the cyclooxygenase enzyme. Hopefully, in the future, compound 5l could serve as a lead compound for developing new COX-2 inhibitors.
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Affiliation(s)
- Ankita Rathore
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Jamia Hamdard (Hamdard University), New Delhi, India
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14
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Fu G, Sivaprakasam P, Dale OR, Manly SP, Cutler SJ, Doerksen RJ. Pharmacophore Modeling, Ensemble Docking, Virtual Screening, and Biological Evaluation on Glycogen Synthase Kinase-3β. Mol Inform 2014; 33:610-26. [PMID: 27486080 DOI: 10.1002/minf.201400044] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 07/23/2014] [Indexed: 12/20/2022]
Abstract
Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. GSK-3β, also known as tau protein kinase I (TPK-I), is one of the most important kinases implicated in the hyperphosphorylation of tau that leads to neurodegenerative diseases. Hence, GSK-3β has emerged as an important therapeutic target. To identify compounds that are structurally novel and diverse compared to previously reported ATP-competitive GSK-3β inhibitors, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to induced-fit effects were explored. An enrichment study was employed to verify the robustness of ensemble docking, using 13 X-ray structures of GSK-3β, compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening underwent biological validation. The bioassay results showed that 15 out of the 24 hit compounds are indeed GSK-3β inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization.
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Affiliation(s)
- Gang Fu
- Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, 38677
| | - Prasanna Sivaprakasam
- Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, 38677
| | - Olivia R Dale
- Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, 38677
| | - Susan P Manly
- National Center for Natural Products Research, University of Mississippi, University, MS, 38677. Faser Hall 419, University, MS 38677, USA phone: (662)-915-5880
| | - Stephen J Cutler
- Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, 38677.,National Center for Natural Products Research, University of Mississippi, University, MS, 38677. Faser Hall 419, University, MS 38677, USA phone: (662)-915-5880
| | - Robert J Doerksen
- Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, 38677. .,National Center for Natural Products Research, University of Mississippi, University, MS, 38677. Faser Hall 419, University, MS 38677, USA phone: (662)-915-5880.
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15
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Webb SJ, Hanser T, Howlin B, Krause P, Vessey JD. Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity. J Cheminform 2014; 6:8. [PMID: 24661325 PMCID: PMC3997921 DOI: 10.1186/1758-2946-6-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 03/18/2014] [Indexed: 01/14/2023] Open
Abstract
Background A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints. A fragmentation algorithm is utilised to investigate the model’s behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model’s behaviour for the specific query. Results Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. Conclusion This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.
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Affiliation(s)
- Samuel J Webb
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Holbeck, Leeds LS11 5PY UK.
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16
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Riniker S, Landrum GA. Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods. J Cheminform 2013; 5:43. [PMID: 24063533 PMCID: PMC3852750 DOI: 10.1186/1758-2946-5-43] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 07/23/2013] [Indexed: 02/03/2023] Open
Abstract
Fingerprint similarity is a common method for comparing chemical structures. Similarity is an appealing approach because, with many fingerprint types, it provides intuitive results: a chemist looking at two molecules can understand why they have been determined to be similar. This transparency is partially lost with the fuzzier similarity methods that are often used for scaffold hopping and tends to vanish completely when molecular fingerprints are used as inputs to machine-learning (ML) models. Here we present similarity maps, a straightforward and general strategy to visualize the atomic contributions to the similarity between two molecules or the predicted probability of a ML model. We show the application of similarity maps to a set of dopamine D3 receptor ligands using atom-pair and circular fingerprints as well as two popular ML methods: random forests and naïve Bayes. An open-source implementation of the method is provided.
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Affiliation(s)
- Sereina Riniker
- Novartis Institutes for BioMedical Research, Basel, Switzerland.
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17
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Stålring J, Almeida PR, Carlsson L, Helgee Ahlberg E, Hasselgren C, Boyer S. Localized Heuristic Inverse Quantitative Structure Activity Relationship with Bulk Descriptors Using Numerical Gradients. J Chem Inf Model 2013; 53:2001-17. [DOI: 10.1021/ci400281y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jonna Stålring
- Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden
| | - Pedro R. Almeida
- EngInMotion Ltd,
Avenida Infante
D. Henrique, n. 145, 3510-070 Viseu, Portugal
| | - Lars Carlsson
- Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden
| | - Ernst Helgee Ahlberg
- Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden
| | - Catrin Hasselgren
- Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden
| | - Scott Boyer
- Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden
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18
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Wang W, He W, Zhou X, Chen X. Optimization of molecular docking scores with support vector rank regression. Proteins 2013; 81:1386-98. [PMID: 23504920 DOI: 10.1002/prot.24282] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2012] [Revised: 01/29/2013] [Accepted: 02/26/2013] [Indexed: 01/16/2023]
Abstract
This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5-66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414 vs. 2.1162 Å) for the top ranked conformations. In compound library screening (LS) tests, an average of 46.3% improvement (18.2-26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to improve comparably the accuracy of LS and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China
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19
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Secci D, Bolasco A, D'Ascenzio M, della Sala F, Yáñez M, Carradori S. Conventional and Microwave-Assisted Synthesis of Benzimidazole Derivatives and TheirIn VitroInhibition of Human Cyclooxygenase. J Heterocycl Chem 2012. [DOI: 10.1002/jhet.1058] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Daniela Secci
- Dipartimento di Chimica e Tecnologie del Farmaco; Università degli Studi di Roma “La Sapienza”; P.le A. Moro 5; 00185; Rome; Italy
| | - Adriana Bolasco
- Dipartimento di Chimica e Tecnologie del Farmaco; Università degli Studi di Roma “La Sapienza”; P.le A. Moro 5; 00185; Rome; Italy
| | - Melissa D'Ascenzio
- Dipartimento di Chimica e Tecnologie del Farmaco; Università degli Studi di Roma “La Sapienza”; P.le A. Moro 5; 00185; Rome; Italy
| | - Flavio della Sala
- Dipartimento di Chimica e Tecnologie del Farmaco; Università degli Studi di Roma “La Sapienza”; P.le A. Moro 5; 00185; Rome; Italy
| | - Matilde Yáñez
- Departamento de Farmacología and Instituto de Farmacia Industrial; Universidad de Santiago de Compostela, E-15782 Santiago de Compostela (La Coruña); Spain
| | - Simone Carradori
- Dipartimento di Chimica e Tecnologie del Farmaco; Università degli Studi di Roma “La Sapienza”; P.le A. Moro 5; 00185; Rome; Italy
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20
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Marcou G, Horvath D, Solov'ev V, Arrault A, Vayer P, Varnek A. Interpretability of SAR/QSAR Models of any Complexity by Atomic Contributions. Mol Inform 2012; 31:639-42. [DOI: 10.1002/minf.201100136] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2011] [Accepted: 05/29/2012] [Indexed: 01/22/2023]
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21
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Reutlinger M, Schneider G. Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. J Mol Graph Model 2012; 34:108-17. [PMID: 22326864 DOI: 10.1016/j.jmgm.2011.12.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2011] [Revised: 12/13/2011] [Accepted: 12/14/2011] [Indexed: 01/29/2023]
Abstract
Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.
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Affiliation(s)
- Michael Reutlinger
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Zurich, Switzerland
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22
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Sanders MPA, Verhoeven S, de Graaf C, Roumen L, Vroling B, Nabuurs SB, de Vlieg J, Klomp JPG. Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs. J Chem Inf Model 2011; 51:2277-92. [PMID: 21866955 DOI: 10.1021/ci200088d] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.
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Affiliation(s)
- Marijn P A Sanders
- Computational Drug Discovery Group, CMBI, Radboud University Nijmegen, Nijmegen, The Netherlands
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23
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Subramaniam S, Mehrotra M, Gupta D. Support Vector Machine Based Classification Model for Screening Plasmodium falciparum Proliferation Inhibitors and Non-Inhibitors. Biomed Eng Comput Biol 2011. [DOI: 10.4137/becb.s7503] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
There is an urgent need to develop novel anti-malarials in view of the increasing disease burden and growing resistance of the currently used drugs against the malarial parasites. Proliferation inhibitors targeting P. falciparum intraerythrocytic cycle are one of the important classes of compounds being explored for its potential to be novel antimalarials. Support Vector Machine (SVM) based model developed by us can facilitate rapid screening of large and diverse chemical libraries by reducing false hits and prioritising compounds before setting up expensive High Throughput Screening experiment. The SVM model, trained with molecular descriptors of proliferation inhibitors and non-inhibitors, displayed a satisfactory performance on cross validations and independent data set, with an average accuracy of 83% and AUC of 0.88. Intriguingly, the method displayed remarkable accuracy for the recently submitted P. falciparum whole cell screening datasets. The method also predicted several inhibitors in the National Cancer Institute diversity set, mostly similar to the known inhibitors.
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Affiliation(s)
- Sangeetha Subramaniam
- Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi, India
| | - Monica Mehrotra
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
| | - Dinesh Gupta
- Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi, India
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24
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Abstract
This chapter reviews the application of fragment descriptors at different stages of virtual screening: filtering, similarity search, and direct activity assessment using QSAR/QSPR models. Several case studies are considered. It is demonstrated that the power of fragment descriptors stems from their universality, very high computational efficiency, simplicity of interpretation, and versatility.
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Affiliation(s)
- Alexandre Varnek
- Laboratory of Chemoinformatics, UMR7177 CNRS, University of Strasbourg, Strasbourg, France
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25
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Hasegawa K, Keiya M, Funatsu K. Visualization of Molecular Selectivity and Structure Generation for Selective Dopamine Inhibitors. Mol Inform 2010; 29:793-800. [DOI: 10.1002/minf.201000096] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2010] [Accepted: 10/17/2001] [Indexed: 11/08/2022]
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26
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Rao H, Li Z, Li X, Ma X, Ung C, Li H, Liu X, Chen Y. Identification of small molecule aggregators from large compound libraries by support vector machines. J Comput Chem 2010; 31:752-63. [PMID: 19569201 DOI: 10.1002/jcc.21347] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries. We used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which showed comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which were correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which were predicted as aggregators. SVM showed slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM showed substantial capability in identifying aggregators from large libraries at low false-hit rates.
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Affiliation(s)
- Hanbing Rao
- College of Chemistry, Sichuan University, Chengdu 610064, People's Republic of China
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27
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28
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Gedeck P, Kramer C, Ertl P. Computational analysis of structure-activity relationships. PROGRESS IN MEDICINAL CHEMISTRY 2010; 49:113-60. [PMID: 20855040 DOI: 10.1016/s0079-6468(10)49004-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Peter Gedeck
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland
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29
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Carlsson L, Helgee EA, Boyer S. Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data. J Chem Inf Model 2009; 49:2551-8. [DOI: 10.1021/ci9002206] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Lars Carlsson
- Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden
| | | | - Scott Boyer
- Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden
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30
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Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform 2009; 10:579-91. [PMID: 19433475 DOI: 10.1093/bib/bbp023] [Citation(s) in RCA: 152] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.
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Affiliation(s)
- Chun Meng Song
- Institute for Infocomm Research, Connexis South Tower, Singapore 138632
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31
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Hofmann B, Franke L, Proschak E, Tanrikulu Y, Schneider P, Steinhilber D, Schneider G. Scaffold-Hopping Cascade Yields Potent Inhibitors of 5-Lipoxygenase. ChemMedChem 2008; 3:1535-8. [DOI: 10.1002/cmdc.200800153] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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32
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Barillari C, Marcou G, Rognan D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model 2008; 48:1396-410. [PMID: 18570371 DOI: 10.1021/ci800064z] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.
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Affiliation(s)
- Caterina Barillari
- Bioinformatics of the Drug, UMR 7175 CNRS-ULP (Universite Louis Pasteur-Strasbourg I), 74 route du Rhin, B.P. 24, F-67400 Illkirch, France
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Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, Low BC, Chen YZ. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J Chem Inf Model 2008; 48:1227-37. [PMID: 18533644 DOI: 10.1021/ci800022e] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.
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Affiliation(s)
- X H Ma
- Centre for Computational Science and Engineering, National University of Singapore, Singapore
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34
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Askjaer S, Langgård M. Combining Pharmacophore Fingerprints and PLS-Discriminant Analysis for Virtual Screening and SAR Elucidation. J Chem Inf Model 2008; 48:476-88. [DOI: 10.1021/ci700356w] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sune Askjaer
- Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark, and Department of Computational Chemistry, H. Lundbeck A/S, Ottiliavej 9, DK-2500 Copenhagen, Valby, Denmark
| | - Morten Langgård
- Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark, and Department of Computational Chemistry, H. Lundbeck A/S, Ottiliavej 9, DK-2500 Copenhagen, Valby, Denmark
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35
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Bonachéra F, Horvath D. Fuzzy tricentric pharmacophore fingerprints. 2. Application of topological fuzzy pharmacophore triplets in quantitative structure-activity relationships. J Chem Inf Model 2008; 48:409-25. [PMID: 18254617 DOI: 10.1021/ci7003237] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Topological fuzzy pharmacophore triplets (2D-FPT), using the number of interposed bonds to measure separation between the atoms representing pharmacophore types, were employed to establish and validate quantitative structure-activity relationships (QSAR). Thirteen data sets for which state-of-the-art QSAR models were reported in literature were revisited in order to benchmark 2D-FPT biological activity-explaining propensities. Linear and nonlinear QSAR models were constructed for each compound series (following the original author's splitting into training/validation subsets) with three different 2D-FPT versions, using the genetic algorithm-driven Stochastic QSAR sampler (SQS) to pick relevant triplets and fit their coefficients. 2D-FPT QSARs are computationally cheap, interpretable, and perform well in benchmarking. In a majority of cases (10/13), default 2D-FPT models validated better than or as well as the best among those reported, including 3D overlay-dependent approaches. Most of the analogues series, either unaffected by protonation equilibria or unambiguously adopting expected protonation states, were equally well described by rule- or pKa-based pharmacophore flagging. Thermolysin inhibitors represent a notable exception: pKa-based flagging boosts model quality, although--surprisingly--not due to proteolytic equilibrium effects. The optimal degree of 2D-FPT fuzziness is compound set dependent. This work further confirmed the higher robustness of nonlinear over linear SQS models. In spite of the wealth of studied sets, benchmarking is nevertheless flawed by low intraset diversity: a whole series of thereby caused artifacts were evidenced, implicitly raising questions about the way QSAR studies are conducted nowadays. An in-depth investigation of thrombin inhibition models revealed that some of the selected triplets make sense (one of these stands for a topological pharmacophore covering the P1 and P2 binding pockets). Nevertheless, equations were either unable to predict the activity of the structurally different ligands or tended to indiscriminately predict any compound outside the training family to be active. 2D-FPT QSARs do however not depend on any common scaffold required for molecule superimposition and may in principle be trained on hand of diverse sets, which is a must in order to obtain widely applicable models. Adding (assumed) inactives of various families for training enabled discovery of models that specifically recognize the structurally different actives.
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Affiliation(s)
- Fanny Bonachéra
- UMR 8576 CNRS-Unité de Glycobiologie Structurale & Fonctionnelle, Université des Sciences et Technologies de Lille, Bât. C9-59655 Villeneuve d'Ascq Cedex, France
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36
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Renner S, Derksen S, Radestock S, Mörchen F. Maximum Common Binding Modes (MCBM): Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints. J Chem Inf Model 2008; 48:319-32. [DOI: 10.1021/ci7003626] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Steffen Renner
- Chemical R&D, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, D-60318 Frankfurt am Main, Germany, Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität, Siesmayerstrasse 70, D-60323 Frankfurt am Main, Germany, and Siemens Corporate Research, 755 College Road East, Princeton, New Jersey 08540
| | - Swetlana Derksen
- Chemical R&D, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, D-60318 Frankfurt am Main, Germany, Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität, Siesmayerstrasse 70, D-60323 Frankfurt am Main, Germany, and Siemens Corporate Research, 755 College Road East, Princeton, New Jersey 08540
| | - Sebastian Radestock
- Chemical R&D, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, D-60318 Frankfurt am Main, Germany, Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität, Siesmayerstrasse 70, D-60323 Frankfurt am Main, Germany, and Siemens Corporate Research, 755 College Road East, Princeton, New Jersey 08540
| | - Fabian Mörchen
- Chemical R&D, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, D-60318 Frankfurt am Main, Germany, Institut für Organische Chemie und Chemische Biologie, Johann Wolfgang Goethe-Universität, Siesmayerstrasse 70, D-60323 Frankfurt am Main, Germany, and Siemens Corporate Research, 755 College Road East, Princeton, New Jersey 08540
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Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, Chen YZ. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model 2007; 26:1276-86. [PMID: 18218332 DOI: 10.1016/j.jmgm.2007.12.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2007] [Revised: 12/05/2007] [Accepted: 12/05/2007] [Indexed: 01/04/2023]
Abstract
Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.
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Affiliation(s)
- L Y Han
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
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Li H, Yap CW, Ung CY, Xue Y, Li ZR, Han LY, Lin HH, Chen YZ. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci 2007; 96:2838-60. [PMID: 17786989 DOI: 10.1002/jps.20985] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.
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Affiliation(s)
- H Li
- Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
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Hou T, Wang J, Li Y. ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine. J Chem Inf Model 2007; 47:2408-15. [DOI: 10.1021/ci7002076] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Tingjun Hou
- Department of Chemistry and Biochemistry, Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, California 92093
| | - Junmei Wang
- Encysive Pharmaceuticals, Inc., 7000 Fannin St., Houston, Texas 77030
| | - Youyong Li
- Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125
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40
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Renner S, Hechenberger M, Noeske T, Böcker A, Jatzke C, Schmuker M, Parsons C, Weil T, Schneider G. Suche nach Wirkstoff-Grundgerüsten mit 3D-Pharmakophorhypothesen und Ensembles neuronaler Netze. Angew Chem Int Ed Engl 2007. [DOI: 10.1002/ange.200604125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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41
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Renner S, Hechenberger M, Noeske T, Böcker A, Jatzke C, Schmuker M, Parsons CG, Weil T, Schneider G. Searching for Drug Scaffolds with 3D Pharmacophores and Neural Network Ensembles. Angew Chem Int Ed Engl 2007; 46:5336-9. [PMID: 17604383 DOI: 10.1002/anie.200604125] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Steffen Renner
- Chemical R&D, Medicinal Chemistry/Cheminformatics, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, 60318 Frankfurt am Main, Germany
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42
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Grant MA, Shanmugasundaram K, Rigby AC. Conotoxin therapeutics: a pipeline for success? Expert Opin Drug Discov 2007; 2:453-68. [DOI: 10.1517/17460441.2.4.453] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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43
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Byvatov E, Baringhaus KH, Schneider G, Matter H. A Virtual Screening Filter for Identification of Cytochrome P450 2C9 (CYP2C9) Inhibitors. ACTA ACUST UNITED AC 2007. [DOI: 10.1002/qsar.200630143] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Abstract
Although the term virtual screening as the in silico analog of high throughput screening has been coined only a decade ago, virtual screening is now a widespread lead identification method in the pharmaceutical industry. A myriad of different methods have been developed exploiting the growing library of target structures and assay data as a basis for finding new lead structures. Exploiting synergies between different methods best utilizes the information available and is at the center of recent developments.
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Affiliation(s)
- Ingo Muegge
- Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, P.O. Box 368, CT 06877-368, USA
| | - Scott Oloff
- Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, P.O. Box 368, CT 06877-368, USA
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Fara DC, Oprea TI, Prossnitz ER, Bologa CG, Edwards BS, Sklar LA. Integration of virtual and physical screening. DRUG DISCOVERY TODAY. TECHNOLOGIES 2006; 3:377-385. [PMID: 38620118 PMCID: PMC7105924 DOI: 10.1016/j.ddtec.2006.11.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
High-throughput screening (HTS) represents the dominant technique for the identification of new lead compounds in current drug discovery. It consists of physical screening (PS) of large libraries of chemicals against one or more specific biological targets. Virtual screening (VS) is a strategy for in silico evaluation of chemical libraries for a given target, and can be integrated to focus the PS process. The present work addresses the integration of both PS and VS, respectively.
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Affiliation(s)
- Dan C. Fara
- Department of Biochemistry and Molecular Biology, Division of Biocomputing, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
| | - Tudor I. Oprea
- Department of Biochemistry and Molecular Biology, Division of Biocomputing, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
| | - Eric R. Prossnitz
- Department of Cell Biology and Physiology, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
| | - Cristian G. Bologa
- Department of Biochemistry and Molecular Biology, Division of Biocomputing, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
| | - Bruce S. Edwards
- Cancer Research and Treatment Center and Department of Pathology, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
| | - Larry A. Sklar
- Cancer Research and Treatment Center and Department of Pathology, University of New Mexico, Health Sciences Center, Albuquerque, NM 87131, USA
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46
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Glen R, Adams S. Similarity Metrics and Descriptor Spaces – Which Combinations to Choose? ACTA ACUST UNITED AC 2006. [DOI: 10.1002/qsar.200610097] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Bender A, Jenkins JL, Li Q, Adams SE, Cannon EO, Glen RC. Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR. ACTA ACUST UNITED AC 2006; 2:141-168. [PMID: 32362803 PMCID: PMC7185533 DOI: 10.1016/s1574-1400(06)02009-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
This chapter discusses recent developments in some of the areas that exploit the molecular similarity principle, novel approaches to capture molecular properties by the use of novel descriptors, focuses on a crucial aspect of computational models-their validity, and discusses additional ways to examine data available, such as those from high-throughput screening (HTS) campaigns and to gain more knowledge from this data. The chapter also presents some of the recent applications of methods discussed focusing on the successes of virtual screening applications, database clustering and comparisons (such as drug- and in-house-likeness), and the recent large-scale validations of docking and scoring programs. While a great number of descriptors and modeling methods has been proposed until today, the recent trend toward proper model validation is very much appreciated. Although some of their limitations are surely because of underlying principles and limitations of fundamental concepts, others will certainly be eliminated in the future.
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Affiliation(s)
- Andreas Bender
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.,Lead Discovery Center, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Jeremy L Jenkins
- Lead Discovery Center, Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Qingliang Li
- College of Chemistry and Molecular Engineering, Center for Theoretical Biology, Peking University, Beijing 100871, China
| | - Sam E Adams
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Edward O Cannon
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Robert C Glen
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
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Bonachéra F, Parent B, Barbosa F, Froloff N, Horvath D. Fuzzy Tricentric Pharmacophore Fingerprints. 1. Topological Fuzzy Pharmacophore Triplets and Adapted Molecular Similarity Scoring Schemes. J Chem Inf Model 2006; 46:2457-77. [PMID: 17125187 DOI: 10.1021/ci6002416] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
This paper introduces a novel molecular description--topological (2D) fuzzy pharmacophore triplets, 2D-FPT--using the number of interposed bonds as the measure of separation between the atoms representing pharmacophore types (hydrophobic, aromatic, hydrogen-bond donor and acceptor, cation, and anion). 2D-FPT features three key improvements with respect to the state-of-the-art pharmacophore fingerprints: (1) The first key novelty is fuzzy mapping of molecular triplets onto the basis set of pharmacophore triplets: unlike in the binary scheme where an atom triplet is set to highlight the bit of a single, best-matching basis triplet, the herein-defined fuzzy approach allows for gradual mapping of each atom triplet onto several related basis triplets, thus minimizing binary classification artifacts. (2) The second innovation is proteolytic equilibrium dependence, by explicitly considering all of the conjugated acids and bases (microspecies). 2D-FPTs are concentration-weighted (as predicted at pH=7.4) averages of microspecies fingerprints. Therefore, small structural modifications, not affecting the overall pharmacophore pattern (in the sense of classical rule-based assignment), but nevertheless triggering a pKa shift, will have a major impact on 2D-FPT. Pairs of almost identical compounds with significantly differing activities ("activity cliffs" in classical descriptor spaces) were in many cases predictable by 2D-FPT. (3) The third innovation is a new similarity scoring formula, acknowledging that the simultaneous absence of a triplet in two molecules is a less-constraining indicator of similarity than its simultaneous presence. It displays excellent neighborhood behavior, outperforming 2D or 3D two-point pharmacophore descriptors or chemical fingerprints. The 2D-FPT calculator was developed using the chemoinformatics toolkit of ChemAxon (www.chemaxon.com).
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Affiliation(s)
- Fanny Bonachéra
- Unite Mixte de Recherche 8576 Centre Nationale de la Recherche Scientifique - Unité de Glycobiologie Structurale & Fonctionnelle, Université des Sciences et Technologies de Lille, Bât. C9-59655 Villeneuve d'Ascq Cedex, France
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