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de Azevedo WF, Quiroga R, Villarreal MA, da Silveira NJF, Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of machine-learning models to explore the scoring function space. J Comput Chem 2024; 45:2333-2346. [PMID: 38900052 DOI: 10.1002/jcc.27449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/04/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024]
Abstract
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
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Affiliation(s)
| | - Rodrigo Quiroga
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Marcos Ariel Villarreal
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | | | | | - Amauri Duarte da Silva
- Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | | | | | - Marco Tutone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
| | | | | | | | - Stéphaine Baud
- Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France
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2
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Bitencourt-Ferreira G, Villarreal MA, Quiroga R, Biziukova N, Poroikov V, Tarasova O, de Azevedo Junior WF. Exploring Scoring Function Space: Developing Computational Models for Drug Discovery. Curr Med Chem 2024; 31:2361-2377. [PMID: 36944627 DOI: 10.2174/0929867330666230321103731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/15/2022] [Accepted: 12/29/2022] [Indexed: 03/23/2023]
Abstract
BACKGROUND The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. OBJECTIVE Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. METHODS We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. RESULTS The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. CONCLUSION The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.
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Affiliation(s)
| | - Marcos A Villarreal
- CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Rodrigo Quiroga
- CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Nadezhda Biziukova
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Olga Tarasova
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Walter F de Azevedo Junior
- Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil
- Specialization Program in Bioinformatics, The Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681 Porto Alegre / RS 90619-900, Brazil
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3
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Ferdous N, Reza MN, Hossain MU, Mahmud S, Napis S, Chowdhury K, Mohiuddin AKM. Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists. PLoS One 2023; 18:e0287179. [PMID: 37352252 PMCID: PMC10289339 DOI: 10.1371/journal.pone.0287179] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 05/31/2023] [Indexed: 06/25/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.
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Affiliation(s)
- Nadim Ferdous
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh
| | - Mahjerin Nasrin Reza
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh
| | - Mohammad Uzzal Hossain
- Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
- Bioinformatics Division, National Institute of Biotechnology, Ashulia, Ganakbari, Savar, Dhaka, Bangladesh
| | - Shahin Mahmud
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh
| | - Suhami Napis
- Department of Molecular Biology, Universiti Putra Malaysia, Serdang, Selangor D.E., Malaysia
| | - Kamal Chowdhury
- Biology Department, Claflin University, Orangeburg, SC, United States of America
| | - A. K. M. Mohiuddin
- Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh
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4
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Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
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Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
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5
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Farnoud A, Ohnmacht AJ, Meinel M, Menden MP. Can artificial intelligence accelerate preclinical drug discovery and precision medicine? Expert Opin Drug Discov 2022; 17:661-665. [PMID: 35708267 DOI: 10.1080/17460441.2022.2090540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Precision medicine leverages molecular biomarkers for selecting optimal treatment strategies. Accordingly, stratifying patients into responder and non-responder has strongly accelerated drug discovery and drug approvals in the last two decades. Recently, the applications of artificial intelligence (AI) in healthcare have been promoting these processes, and continue to improve patient care through systematically analysing large-scale molecular data and electronic health records. In particular, preclinical pharmacogenomics data empowered AI to unfold its full potential.Areas covered: Here, we discuss the opportunities of AI in pharmacogenomics, drug discovery and precision medicine. In particular, we shed some light on the advancements in computational biomedicine from statistical, machine learning (ML) to complex deep learning (DL) models.Expert opinion: AI has already strongly impacted drug discovery, and will continue to revolutionise academic research and the pharmaceutical industry. Its algorithms aid the identification of novel treatment options through molecular signatures and thus pave the way for the next generation of precision medicine.
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Affiliation(s)
- Ali Farnoud
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Martin Meinel
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Dermatology and Allergy, Technical University Munich, Munich, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany.,German Center for Diabetes Research (DZD e.V.), Munich, Germany
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6
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Zhao L, Zhu Y, Wang J, Wen N, Wang C, Cheng L. A brief review of protein-ligand interaction prediction. Comput Struct Biotechnol J 2022; 20:2831-2838. [PMID: 35765652 PMCID: PMC9189993 DOI: 10.1016/j.csbj.2022.06.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 01/21/2023] Open
Abstract
The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.
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Affiliation(s)
- Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Yan Zhu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Naifeng Wen
- School of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
- Corresponding authors.
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin, China
- Corresponding authors.
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7
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Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Sci Rep 2022; 12:7843. [PMID: 35551258 PMCID: PMC9096754 DOI: 10.1038/s41598-022-11879-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
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8
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Karasev DA, Sobolev BN, Lagunin AA, Filimonov DA, Poroikov VV. The method predicting interaction between protein targets and small-molecular ligands with the wide applicability domain. Comput Biol Chem 2022; 98:107674. [DOI: 10.1016/j.compbiolchem.2022.107674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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9
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Rezaei MA, Li Y, Wu D, Li X, Li C. Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:407-417. [PMID: 33360998 PMCID: PMC8942327 DOI: 10.1109/tcbb.2020.3046945] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.
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Affiliation(s)
- Mohammad A. Rezaei
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
| | - Yanjun Li
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Dapeng Wu
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Xiaolin Li
- Cognization Lab, Palo Alto, California, USA
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
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10
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Malik AA, Ojha SC, Schaduangrat N, Nantasenamat C. ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors. Mol Divers 2021; 26:467-487. [PMID: 34609711 DOI: 10.1007/s11030-021-10292-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 08/03/2021] [Indexed: 10/20/2022]
Abstract
Alzheimer's disease (AD) is one of the most common forms of dementia and is associated with a decline in cognitive function and language ability. The deficiency of the cholinergic neurotransmitter known as acetylcholine (ACh) is associated with AD. Acetylcholinesterase (AChE) hydrolyses ACh and inhibits the cholinergic transmission. Furthermore, both AChE and butyrylcholinesterase (BChE) plays important roles in early and late stages of AD. Therefore, the inhibition of either or both cholinesterase enzymes represent a promising therapeutic route for treating AD. In this study, a large-scale classification structure-activity relationship model was developed to predict cholinesterase inhibitory activities as well as revealing important substructures governing their activities. Herein, a non-redundant dataset constituting 985 and 1056 compounds for AChE and BChE, respectively, was obtained from the ChEMBL database. These inhibitors were described by 12 sets of molecular fingerprints and predictive models were developed using the random forest algorithm. Evaluation of the model performance by means of Matthews correlation coefficient and consideration of the model's interpretability indicated that the SubstructureCount fingerprint was the most robust with five-fold cross-validated MCC of [0.76, 0.82] for AChE and BChE, respectively, and test MCC of [0.73, 0.97]. Feature interpretation revealed that the aromatic ring system, heterocyclic nitrogen containing compounds and amines are important for cholinesterase inhibition. Finally, the model was deployed as a publicly available webserver called the ABCpred at http://codes.bio/abcpred/ .
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Affiliation(s)
- Aijaz Ahmad Malik
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Suvash Chandra Ojha
- Department of Infectious Diseases, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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11
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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12
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Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS. Curr Med Chem 2021; 28:1746-1756. [PMID: 32410551 DOI: 10.2174/0929867327666200515101820] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. OBJECTIVE Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. METHODS SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions. RESULTS Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. CONCLUSION Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
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Affiliation(s)
| | - Camila Rizzotto
- Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil
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13
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Adeshina YO, Deeds EJ, Karanicolas J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc Natl Acad Sci U S A 2020; 117:18477-18488. [PMID: 32669436 PMCID: PMC7414157 DOI: 10.1073/pnas.2000585117] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50 better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC50 280 nM, corresponding to Ki of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.
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Affiliation(s)
- Yusuf O Adeshina
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111
- Center for Computational Biology, University of Kansas, Lawrence, KS 66045
| | - Eric J Deeds
- Center for Computational Biology, University of Kansas, Lawrence, KS 66045
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111;
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14
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Fresnais L, Ballester PJ. The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Brief Bioinform 2020; 22:5855396. [PMID: 32568385 DOI: 10.1093/bib/bbaa095] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 12/20/2022] Open
Abstract
Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets; the difference was not significant in the remaining two targets). A 3-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.
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15
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Malik AA, Phanus-Umporn C, Schaduangrat N, Shoombuatong W, Isarankura-Na-Ayudhya C, Nantasenamat C. HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors. J Comput Chem 2020; 41:1820-1834. [PMID: 32449536 DOI: 10.1002/jcc.26223] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/10/2020] [Accepted: 04/28/2020] [Indexed: 02/06/2023]
Abstract
Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure-activity relationship (CSAR) model for identifying substructures giving rise to anti-HCV activities among a set of 578 non-redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in-depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.
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Affiliation(s)
- Aijaz Ahmad Malik
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Chuleeporn Phanus-Umporn
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | | | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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16
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Li H, Sze K, Lu G, Ballester PJ. Machine‐learning scoring functions for structure‐based virtual screening. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1478] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Hongjian Li
- Cancer Research Center of Marseille (INSERM U1068, Institut Paoli‐Calmettes, Aix‐Marseille Université UM105, CNRS UMR7258) Marseille France
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Kam‐Heung Sze
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Gang Lu
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Pedro J. Ballester
- Cancer Research Center of Marseille (INSERM U1068, Institut Paoli‐Calmettes, Aix‐Marseille Université UM105, CNRS UMR7258) Marseille France
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17
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Li H, Sze K, Lu G, Ballester PJ. Machine‐learning scoring functions for structure‐based drug lead optimization. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1465] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Hongjian Li
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Kam‐Heung Sze
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Gang Lu
- CUHK‐SDU Joint Laboratory on Reproductive Genetics, School of Biomedical Sciences Chinese University of Hong Kong Shatin Hong Kong
| | - Pedro J. Ballester
- Cancer Research Center of Marseille (INSERM U1068, Institut Paoli‐Calmettes, Aix‐Marseille Université UM105, CNRS UMR7258) Marseille France
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