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Yan J, Ye Z, Yang Z, Lu C, Zhang S, Liu Q, Qiu J. Multi-task bioassay pre-training for protein-ligand binding affinity prediction. Brief Bioinform 2023; 25:bbad451. [PMID: 38084920 PMCID: PMC10783875 DOI: 10.1093/bib/bbad451] [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: 08/15/2023] [Revised: 10/27/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.
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Affiliation(s)
- Jiaxian Yan
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, JinZhai Road, 230026, Anhui, China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Tencent, Shennan Road, 518057, Guangdong, China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Tencent, Shennan Road, 518057, Guangdong, China
| | - Chengqiang Lu
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, JinZhai Road, 230026, Anhui, China
| | - Shengyu Zhang
- Tencent Quantum Laboratory, Tencent, Shennan Road, 518057, Guangdong, China
| | - Qi Liu
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, JinZhai Road, 230026, Anhui, China
| | - Jiezhong Qiu
- Tencent Quantum Laboratory, Tencent, Shennan Road, 518057, Guangdong, China
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2
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Shiota K, Akutsu T. Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad155. [PMID: 37928345 PMCID: PMC10625475 DOI: 10.1093/bioadv/vbad155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/20/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Motivation Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances. Results To investigate what kind of distance representation is effective for P-L binding affinity prediction, we have developed two algorithms that improved ECIF's feature extraction method to take distance into account. One is multi-shelled ECIF, which takes into account the distance between atoms by dividing the distance between atoms into multiple layers. The other is weighted ECIF, which weights the importance of interactions according to the distance between atoms. A comparison of these two methods shows that multi-shelled ECIF outperforms weighted ECIF and the original ECIF, achieving a CASF-2016 scoring power Pearson correlation coefficient of 0.877. Availability and implementation All the codes and data are available on GitHub (https://github.com/koji11235/MSECIFv2).
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Affiliation(s)
- Koji Shiota
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Tatsuya Akutsu
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan
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3
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McFee M, Kim PM. GDockScore: a graph-based protein-protein docking scoring function. BIOINFORMATICS ADVANCES 2023; 3:vbad072. [PMID: 37359726 PMCID: PMC10290236 DOI: 10.1093/bioadv/vbad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/30/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Summary Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. Availability and implementation The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Matthew McFee
- Department of Molecular Genetics, The University of Toronto, Toronto, ON M5S 1A8, Canada
- Donnelly Centre for Cellular and Biomolecular Research, The University of Toronto, Toronto, ON M5S 3E1, Canada
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4
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Pliushcheuskaya P, Künze G. Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels. Int J Mol Sci 2023; 24:ijms24119226. [PMID: 37298178 DOI: 10.3390/ijms24119226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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Affiliation(s)
- Palina Pliushcheuskaya
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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5
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Gorgulla C, Jayaraj A, Fackeldey K, Arthanari H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr Opin Chem Biol 2022; 69:102156. [PMID: 35576813 PMCID: PMC9990419 DOI: 10.1016/j.cbpa.2022.102156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/16/2022] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
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Affiliation(s)
- Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany; Zuse Institute Berlin, Berlin, Germany
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.
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6
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Can docking scoring functions guarantee success in virtual screening? VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Guedes IA, Barreto AMS, Marinho D, Krempser E, Kuenemann MA, Sperandio O, Dardenne LE, Miteva MA. New machine learning and physics-based scoring functions for drug discovery. Sci Rep 2021; 11:3198. [PMID: 33542326 PMCID: PMC7862620 DOI: 10.1038/s41598-021-82410-1] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.
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Affiliation(s)
- Isabella A Guedes
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.,Inserm U973, Université Paris Diderot, Paris, France
| | - André M S Barreto
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | - Diogo Marinho
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | | | | | - Olivier Sperandio
- Inserm U973, Université Paris Diderot, Paris, France.,Structural Bioinformatics Unit, CNRS UMR3528, Institut Pasteur, 75015, Paris, France
| | - Laurent E Dardenne
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.
| | - Maria A Miteva
- Inserm U973, Université Paris Diderot, Paris, France. .,Inserm U1268 "Medicinal Chemistry and Translational Research", CiTCoM, UMR 8038, CNRS, Université de Paris, 75006, Paris, France.
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8
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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9
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Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. J Comput Aided Mol Des 2020; 34:817-830. [PMID: 32185583 DOI: 10.1007/s10822-020-00305-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 03/07/2020] [Indexed: 10/24/2022]
Abstract
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process.
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10
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Moman E, Grishina MA, Potemkin VA. Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019; 33:943-953. [PMID: 31728812 DOI: 10.1007/s10822-019-00248-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/04/2019] [Indexed: 12/20/2022]
Abstract
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.
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Affiliation(s)
- Edelmiro Moman
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080.
| | - Maria A Grishina
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
| | - Vladimir A Potemkin
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
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11
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Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key Topics in Molecular Docking for Drug Design. Int J Mol Sci 2019; 20:E4574. [PMID: 31540192 PMCID: PMC6769580 DOI: 10.3390/ijms20184574] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 12/18/2022] Open
Abstract
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
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Affiliation(s)
- Pedro H M Torres
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.
| | - Ana C R Sodero
- Department of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Paula Jofily
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Floriano P Silva-Jr
- Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil.
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12
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Guedes IA, Pereira FSS, Dardenne LE. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges. Front Pharmacol 2018; 9:1089. [PMID: 30319422 PMCID: PMC6165880 DOI: 10.3389/fphar.2018.01089] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 09/07/2018] [Indexed: 12/19/2022] Open
Abstract
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
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Affiliation(s)
- Isabella A Guedes
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Felipe S S Pereira
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Laurent E Dardenne
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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13
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Zhenin M, Bahia MS, Marcou G, Varnek A, Senderowitz H, Horvath D. Rescoring of docking poses under Occam’s Razor: are there simpler solutions? J Comput Aided Mol Des 2018; 32:877-888. [DOI: 10.1007/s10822-018-0155-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 08/26/2018] [Indexed: 01/04/2023]
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