1
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Shen C, Song J, Hsieh CY, Cao D, Kang Y, Ye W, Wu Z, Wang J, Zhang O, Zhang X, Zeng H, Cai H, Chen Y, Chen L, Luo H, Zhao X, Jian T, Chen T, Jiang D, Wang M, Ye Q, Wu J, Du H, Shi H, Deng Y, Hou T. DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery. J Chem Inf Model 2024; 64:5381-5391. [PMID: 38920405 DOI: 10.1021/acs.jcim.4c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.
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Affiliation(s)
- Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Wenling Ye
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hao Zeng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yu Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Linkang Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Hao Luo
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Xinda Zhao
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Qing Ye
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hui Shi
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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2
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Zhang X, Shen C, Zhang H, Kang Y, Hsieh CY, Hou T. Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening. Acc Chem Res 2024; 57:1500-1509. [PMID: 38577892 DOI: 10.1021/acs.accounts.4c00093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.
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Affiliation(s)
- Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Chao Shen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Haotian Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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3
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Wang Z, Wang S, Li Y, Guo J, Wei Y, Mu Y, Zheng L, Li W. A new paradigm for applying deep learning to protein-ligand interaction prediction. Brief Bioinform 2024; 25:bbae145. [PMID: 38581420 PMCID: PMC10998640 DOI: 10.1093/bib/bbae145] [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: 11/06/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 04/08/2024] Open
Abstract
Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.
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Affiliation(s)
- Zechen Wang
- School of Physics, Shandong University, South Shanda Road, 250100 Shandong, China
| | - Sheng Wang
- Shanghai Zelixir Biotech, Xiangke Road, 200030, Shanghai, China
| | - Yangyang Li
- School of Physics, Shandong University, South Shanda Road, 250100 Shandong, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macao, China
| | - Yanjie Wei
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan Road 1068, Shenzhen, 518055 Guang Dong, China
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Liangzhen Zheng
- Shanghai Zelixir Biotech, Xiangke Road, 200030, Shanghai, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Xueyuan Road 1068, Shenzhen, 518055 Guang Dong, China
| | - Weifeng Li
- School of Physics, Shandong University, South Shanda Road, 250100 Shandong, China
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4
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Luo D, Liu D, Qu X, Dong L, Wang B. Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning. J Chem Inf Model 2024; 64:1892-1906. [PMID: 38441880 DOI: 10.1021/acs.jcim.3c01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.
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Affiliation(s)
- Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Dandan Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Xiaoyang Qu
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, P. R. China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine (Putian University), Fujian Province University, Putian 351100, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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5
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Zhang X, Shen C, Hsieh CY, Hou T. Harnessing deep learning for enhanced ligand docking. Trends Pharmacol Sci 2024; 45:103-106. [PMID: 38160084 DOI: 10.1016/j.tips.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024]
Abstract
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.
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Affiliation(s)
- Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Chao Shen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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6
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Cai H, Shen C, Jian T, Zhang X, Chen T, Han X, Yang Z, Dang W, Hsieh CY, Kang Y, Pan P, Ji X, Song J, Hou T, Deng Y. CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training. Chem Sci 2024; 15:1449-1471. [PMID: 38274053 PMCID: PMC10806797 DOI: 10.1039/d3sc05552c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.
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Affiliation(s)
- Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xiaoqi Han
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Zhuo Yang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Wei Dang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiangyang Ji
- Department of Automation, Tsinghua University Beijing 100084 China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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8
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Libouban PY, Aci-Sèche S, Gómez-Tamayo JC, Tresadern G, Bonnet P. The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks. Int J Mol Sci 2023; 24:16120. [PMID: 38003312 PMCID: PMC10671244 DOI: 10.3390/ijms242216120] [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: 09/14/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023] Open
Abstract
Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.
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Affiliation(s)
- Pierre-Yves Libouban
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Samia Aci-Sèche
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
| | - Jose Carlos Gómez-Tamayo
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., B-2340 Beerse, Belgium; (J.C.G.-T.); (G.T.)
| | - Pascal Bonnet
- Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d’Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France; (P.-Y.L.); (S.A.-S.)
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