1
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Prat A, Abdel Aty H, Bastas O, Kamuntavičius G, Paquet T, Norvaišas P, Gasparotto P, Tal R. HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery. J Chem Inf Model 2024; 64:5817-5831. [PMID: 39037942 DOI: 10.1021/acs.jcim.4c00481] [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: 07/24/2024]
Abstract
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.
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Affiliation(s)
- Alvaro Prat
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Hisham Abdel Aty
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Orestis Bastas
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | | | - Tanya Paquet
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Povilas Norvaišas
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Piero Gasparotto
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Roy Tal
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
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2
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Hong Q, Zhou G, Qin Y, Shen J, Li H. SadNet: a novel multimodal fusion network for protein-ligand binding affinity prediction. Phys Chem Chem Phys 2024; 26:12880-12891. [PMID: 38625412 DOI: 10.1039/d3cp05664c] [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: 04/17/2024]
Abstract
Protein-ligand binding affinity prediction plays an important role in the field of drug discovery. Existing deep learning-based approaches have significantly improved the efficiency of protein-ligand binding affinity prediction through their excellent inductive bias capability. However, these methods only focus on fragmented three-dimensional data, which truncates the integrity of pocket data, leading to the neglect of potential long-range interactions. In this paper, we propose a dual-stream framework, with amino acid sequence assisting the atomic data fusion for graph neural network (termed SadNet), to fuse both 3D atomic data and sequence data for more accurate prediction results. In detail, SadNet consists of a pocket module and a sequence module. The sequence module expands the "receptive field" of the pocket module through a mid-term virtual node fusion. To better integrate sequence-level information from the sequence module and 3D structural information from the pocket module, we incorporate structural information for each amino acid within the sequence module. Besides, to better understand the intrinsic relationship between sequences and 3D atomic information, our SadNet utilizes information stacking from both the early stage and later stage. Experimental results on publicly available benchmark datasets demonstrate the superiority of the proposed dual-stream approach over the state-of-the-art alternatives. The code of this work is available online at https://github.com/wardhong/SadNet.
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Affiliation(s)
- Qiansen Hong
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Guoqiang Zhou
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Yuke Qin
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Jun Shen
- University of Wollongong, Australia
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3
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Qu X, Dong L, Luo D, Si Y, Wang B. Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2263-2274. [PMID: 37433009 DOI: 10.1021/acs.jcim.3c00567] [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: 07/13/2023]
Abstract
Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.
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Affiliation(s)
- Xiaoyang Qu
- 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
| | - 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
| | - 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
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, 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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4
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Mqawass G, Popov P. graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction. J Chem Inf Model 2024; 64:2323-2330. [PMID: 38366974 DOI: 10.1021/acs.jcim.3c00771] [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: 02/19/2024]
Abstract
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities. The graphLambda model exhibits superior performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different types of train-validation set partitions. The development of graphLambda underscores the potential of graph neural networks in advancing binding affinity prediction models, contributing to more effective CADD methodologies.
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Affiliation(s)
- Ghaith Mqawass
- Faculty of Computer Science, University of Vienna, Vienna A-1090, Austria
- UniVie Doctoral School Computer Science, University of Vienna, Vienna A-1090, Austria
| | - Petr Popov
- Tetra-d, Rheinweg 9, Schaffhausen 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, Bremen 28759, Germany
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5
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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6
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Rayka M, Mirzaei M, Mohammad Latifi A. An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values. Mol Inform 2024; 43:e202300292. [PMID: 38358080 DOI: 10.1002/minf.202300292] [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: 10/25/2023] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequently, it is crucial to report a measure of confidence and quantify the uncertainty in the model's predictions during test time. Here, we adopt the conformal prediction technique to evaluate the confidence of a prediction for each member of the core set of the CASF 2016 benchmark. The conformal prediction technique requires a diverse ensemble of predictors for uncertainty estimation. To this end, we introduce ENS-Score as an ensemble predictor, which includes 30 models with different protein-ligand representation approaches and achieves Pearson's correlation of 0.842 on the core set of the CASF 2016 benchmark. Also, we comprehensively investigate the residual error of each data point to assess the normality behavior of the distribution of the residual errors and their correlation to the structural features of the ligands, such as hydrophobic interactions and halogen bonding. In the end, we provide a local host web application to facilitate the usage of ENS-Score. All codes to repeat results are provided at https://github.com/miladrayka/ENS_Score.
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Affiliation(s)
- Milad Rayka
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Morteza Mirzaei
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Ali Mohammad Latifi
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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7
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Zhang Y, Li S, Meng K, Sun S. Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction. J Chem Inf Model 2024; 64:1456-1472. [PMID: 38385768 DOI: 10.1021/acs.jcim.3c01841] [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] [Indexed: 02/23/2024]
Abstract
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.
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Affiliation(s)
- Yunjiang Zhang
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shuyuan Li
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Kong Meng
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shaorui Sun
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
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8
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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9
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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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10
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Chen D, Liu J, Wei GW. TopoFormer: Multiscale Topology-enabled Structure-to-Sequence Transformer for Protein-Ligand Interaction Predictions. RESEARCH SQUARE 2024:rs.3.rs-3640878. [PMID: 38405777 PMCID: PMC10889053 DOI: 10.21203/rs.3.rs-3640878/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Pre-trained deep Transformers have had tremendous success in a wide variety of disciplines. However, in computational biology, essentially all Transformers are built upon the biological sequences, which ignores vital stereochemical information and may result in crucial errors in downstream predictions. On the other hand, three-dimensional (3D) molecular structures are incompatible with the sequential architecture of Transformer and natural language processing (NLP) models in general. This work addresses this foundational challenge by a topological Transformer (TopoFormer). TopoFormer is built by integrating NLP and a multiscale topology techniques, the persistent topological hyperdigraph Laplacian (PTHL), which systematically converts intricate 3D protein-ligand complexes at various spatial scales into a NLP-admissible sequence of topological invariants and homotopic shapes. Element-specific PTHLs are further developed to embed crucial physical, chemical, and biological interactions into topological sequences. TopoFormer surges ahead of conventional algorithms and recent deep learning variants and gives rise to exemplary scoring accuracy and superior performance in ranking, docking, and screening tasks in a number of benchmark datasets. The proposed topological sequences can be extracted from all kinds of structural data in data science to facilitate various NLP models, heralding a new era in AI-driven discovery.
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Affiliation(s)
- Dong Chen
- Department of Mathematics, Michigan State University, MI, 48824, USA
| | - Jian Liu
- Department of Mathematics, Michigan State University, MI, 48824, USA
- Mathematical Science Research Center, Chongqing University of Technology, Chongqing 400054, China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI, 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA
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11
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Wang H. Prediction of protein-ligand binding affinity via deep learning models. Brief Bioinform 2024; 25:bbae081. [PMID: 38446737 PMCID: PMC10939342 DOI: 10.1093/bib/bbae081] [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: 11/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
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12
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Wang J, Zhang L, Sun J, Yang X, Wu W, Chen W, Zhao Q. Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints. Methods 2024; 221:18-26. [PMID: 38040204 DOI: 10.1016/j.ymeth.2023.11.014] [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: 10/21/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023] Open
Abstract
Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.
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Affiliation(s)
- Jifeng Wang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi 276000, China
| | - Xin Yang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
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13
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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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14
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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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15
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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16
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Zhao X, Li H, Zhang K, Huang SY. Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information. J Phys Chem B 2023; 127:9021-9034. [PMID: 37822259 DOI: 10.1021/acs.jpcb.3c04421] [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: 10/13/2023]
Abstract
Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.
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Affiliation(s)
- Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Keqiong Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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17
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Dong L, Shi S, Qu X, Luo D, Wang B. Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph. Phys Chem Chem Phys 2023; 25:24110-24120. [PMID: 37655493 DOI: 10.1039/d3cp03651k] [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: 09/02/2023]
Abstract
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.
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Affiliation(s)
- Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Shuai Shi
- Department of Algorithm, TuringQ Co., Ltd., Shanghai, 200240, China
| | - Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen, 361005, China
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18
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Krishna Swaroop A, Krishnan Namboori PK, Esakkimuthukumar M, Praveen TK, Nagarjuna P, Patnaik SK, Selvaraj J. Leveraging decagonal in-silico strategies for uncovering IL-6 inhibitors with precision. Comput Biol Med 2023; 163:107231. [PMID: 37421735 DOI: 10.1016/j.compbiomed.2023.107231] [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: 02/19/2023] [Revised: 06/27/2023] [Accepted: 07/01/2023] [Indexed: 07/10/2023]
Abstract
Interleukin-6 upregulation leads to various acute phase reactions such as local inflammation and systemic inflammation in many diseases like cancer, multiple sclerosis, rheumatoid arthritis, anemia, and Alzheimer's disease stimulating JAK/STAT3, Ras/MAPK, PI3K-PKB/Akt pathogenic pathways. Since no small molecules are available in the market against IL-6 till now, we have designed a class of small bioactive 1,3 - indanedione (IDC) molecules for inhibiting IL-6 using a decagonal approach computational studies. The IL-6 mutations were mapped in the IL-6 protein (PDB ID: 1ALU) from thorough pharmacogenomic and proteomics studies. The protein-drug interaction networking analysis for 2637 FFDA-approved drugs with IL-6 protein using Cytoscape software showed that 14 drugs have prominent interactions with IL-6. Molecular docking studies showed that the designed compound IDC-24 (-11.8 kcal/mol) and methotrexate (-5.20) bound most strongly to the 1ALU south asian population mutated protein. MMGBSA results indicated that IDC-24 (-41.78 kcal/mol) and methotrexate (-36.81 kcal/mol) had the highest binding energy when compared to the standard molecules LMT-28 (-35.87 kcal/mol) and MDL-A (-26.18 kcal/mol). These results we substantiated by the molecular dynamic studies in which the compound IDC-24 and the methotrexate had the highest stability. Further, the MMPBSA computations produced energies of -28 kcal/mol and -14.69 kcal/mol for IDC-24 and LMT-28. KDeep absolute binding affinity computations revealed energies of -5.81 kcal/mol and -4.74 kcal/mol for IDC-24 and LMT-28 respectively. Finally, our decagonal approach established the compound IDC-24 from the designed 1,3-indanedione library and methotrexate from protein drug interaction networking as suitable HITs against IL-6.
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Affiliation(s)
- Akey Krishna Swaroop
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - P K Krishnan Namboori
- Amrita Molecular Modeling and Synthesis (AMMAS) Research Lab, Amrita Vishwavidyapeetham, Amrita Nagar, Ettimadai, Coimbatore, Tamilnadu, India
| | - M Esakkimuthukumar
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - T K Praveen
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Palathoti Nagarjuna
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Sunil Kumar Patnaik
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India.
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19
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Tang M, Li B, Chen H. Application of message passing neural networks for molecular property prediction. Curr Opin Struct Biol 2023; 81:102616. [PMID: 37267824 DOI: 10.1016/j.sbi.2023.102616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 06/04/2023]
Abstract
Accurate molecular property prediction, as one of the classical cheminformatics topics, plays a prominent role in the fields of computer-aided drug design. For instance, property prediction models can be used to quickly screen large molecular libraries to find lead compounds. Message-passing neural networks (MPNNs), a sub-class of Graph neural networks (GNNs), have recently been demonstrated to outperform other deep learning methods on a variety of tasks, including the prediction of molecular characteristics. In this survey, we provide a brief review of the MPNN models and their applications on molecular property prediction.
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Affiliation(s)
- Miru Tang
- Guangzhou Laboratory, Guangzhou, 510005, Guangdong Province, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou, 510530, China; State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Baiqing Li
- Guangzhou Laboratory, Guangzhou, 510005, Guangdong Province, China
| | - Hongming Chen
- Guangzhou Laboratory, Guangzhou, 510005, Guangdong Province, China.
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20
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Zhang X, Shen C, Jiang D, Zhang J, Ye Q, Xu L, Hou T, Pan P, Kang Y. TB-IECS: an accurate machine learning-based scoring function for virtual screening. J Cheminform 2023; 15:63. [PMID: 37403155 DOI: 10.1186/s13321-023-00731-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023] Open
Abstract
Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein-ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.
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Affiliation(s)
- Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Jintu Zhang
- 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
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China
| | - Tingjun Hou
- 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.
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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21
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Zhang S, Jin Y, Liu T, Wang Q, Zhang Z, Zhao S, Shan B. SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction. ACS OMEGA 2023; 8:22496-22507. [PMID: 37396234 PMCID: PMC10308598 DOI: 10.1021/acsomega.3c00085] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/01/2023] [Indexed: 07/04/2023]
Abstract
Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.
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Affiliation(s)
- Shuke Zhang
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- Shijiazhuang
Xianyu Digital Biotechnology Co., Ltd, Shijiazhuang 050024, China
| | - Yanzhao Jin
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- Shijiazhuang
Xianyu Digital Biotechnology Co., Ltd, Shijiazhuang 050024, China
| | - Tianmeng Liu
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- Shijiazhuang
Xianyu Digital Biotechnology Co., Ltd, Shijiazhuang 050024, China
| | - Qi Wang
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- Shijiazhuang
Xianyu Digital Biotechnology Co., Ltd, Shijiazhuang 050024, China
| | - Zhaohui Zhang
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- College
of Computer and Cyber Security, Hebei Normal
University, Shijiazhuang 050024, China
| | - Shuliang Zhao
- College
of Computer and Cyber Security, Hebei Normal
University, Shijiazhuang 050024, China
- Hebei
Provincial Key Laboratory of Network and Information Security, Shijiazhuang 050024, China
- Hebei
Provincial Engineering Research Center for Supply Chain Big Data Analytics
& Data Security, Shijiazhuang 050024, China
| | - Bo Shan
- Software
College, Hebei Normal University, Shijiazhuang 050024, China
- Shijiazhuang
Xianyu Digital Biotechnology Co., Ltd, Shijiazhuang 050024, China
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22
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Rayka M, Firouzi R. GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features. Mol Inform 2023; 42:e2200135. [PMID: 36722733 DOI: 10.1002/minf.202200135] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 02/02/2023]
Abstract
In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance-weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein-ligand complex. GB-Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF-2016 benchmark test in the scoring power metric. GB-Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.
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Affiliation(s)
- Milad Rayka
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
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23
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Jiang D, Ye Z, Hsieh CY, Yang Z, Zhang X, Kang Y, Du H, Wu Z, Wang J, Zeng Y, Zhang H, Wang X, Wang M, Yao X, Zhang S, Wu J, Hou T. MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions. Chem Sci 2023; 14:2054-2069. [PMID: 36845922 PMCID: PMC9945430 DOI: 10.1039/d2sc06576b] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/11/2023] [Indexed: 01/21/2023] Open
Abstract
Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China .,Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China .,College of Computer Science and Technology, Zhejiang University Hangzhou 310006 Zhejiang China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Xujun Zhang
- 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
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 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
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaorui Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and TechnologyMacao
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and TechnologyMacao
| | - Shengyu Zhang
- Tencent Quantum Laboratory, Tencent Shenzhen 518057 Guangdong China
| | - Jian Wu
- College of Computer Science and Technology, Zhejiang University Hangzhou 310006 Zhejiang China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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24
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Giri N, Cheng J. Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge. Biomolecules 2023; 13:biom13010132. [PMID: 36671518 PMCID: PMC9855343 DOI: 10.3390/biom13010132] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.
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25
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Gill J, Moullet M, Martinsson A, Miljković F, Williamson B, Arends RH, Pilla Reddy V. Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction. CPT Pharmacometrics Syst Pharmacol 2022; 11:1560-1568. [PMID: 36176050 PMCID: PMC9755922 DOI: 10.1002/psp4.12870] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 11/08/2022] Open
Abstract
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific data generated from a wide variety of in vitro and in vivo models, which are later refined with clinical data and system-specific parameters. Machine learning has the potential to be utilized for the prediction of drug-drug interactions much earlier in the drug discovery cycle, using inputs derived from, among others, chemical structure. This could lead to refined chemical designs in early drug discovery. Machine-learning models have many advantages, such as the capacity to automate learning (increasing the speed and scalability of predictions), improved generalizability by learning from multicase historical data, and highlighting statistical and potentially clinically significant relationships between input variables. In contrast, the routinely used mechanistic models (physiologically-based pharmacokinetic models and population pharmacokinetics) are currently considered more interpretable, reliable, and require a smaller sample size of data, although insights differ on a case-by-case basis. Therefore, they may be appropriate for later stages of drug-drug interaction assessment when more in vivo and clinical data are available. A combined approach of using mechanistic models to highlight features that can be used for training machine-learning models may also be exploitable in the future to improve the performance of machine learning. In this review, we provide concepts, strategic considerations, and compare machine learning to mechanistic modeling for drug-drug interaction risk assessment across the stages of drug discovery and development.
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Affiliation(s)
- Jaidip Gill
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety SciencesBiopharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Marie Moullet
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety SciencesBiopharmaceuticals R&D, AstraZenecaCambridgeUK
| | - Anton Martinsson
- Imaging and Data Analytics, Clinical Pharmacology & Safety SciencesBiopharmaceuticals R&D, AstraZenecaGothenburgSweden
| | - Filip Miljković
- Imaging and Data Analytics, Clinical Pharmacology & Safety SciencesBiopharmaceuticals R&D, AstraZenecaGothenburgSweden
| | - Beth Williamson
- Oncology DMPKOncology R&D, AstraZenecaCambridgeUK,Present address:
DMPK, UCBSurreyUK
| | - Rosalinda H. Arends
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety SciencesBiopharmaceuticals R&D, AstraZenecaGaithersburgMDUSA,Present address:
Bioinformatics & Data Science, ExelixisAlamedaCAUSA
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety SciencesBiopharmaceuticals R&D, AstraZenecaCambridgeUK
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Réau M, Renaud N, Xue LC, Bonvin AMJJ. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. Bioinformatics 2022; 39:6845451. [PMID: 36420989 PMCID: PMC9805592 DOI: 10.1093/bioinformatics/btac759] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 10/19/2022] [Accepted: 11/23/2022] [Indexed: 11/25/2022] Open
Abstract
MOTIVATION Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. RESULTS We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. AVAILABILITY AND IMPLEMENTATION DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Li C Xue
- Center for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen 6525 GA, The Netherlands
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27
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Vivekanandan S, Vetrivel U, Hanna LE. Design of human immunodeficiency virus-1 neutralizing peptides targeting CD4-binding site: An integrative computational biologics approach. Front Med (Lausanne) 2022; 9:1036874. [DOI: 10.3389/fmed.2022.1036874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
Peptide therapeutics have recently gained momentum in antiviral therapy due to their increased potency and cost-effectiveness. Interaction of the HIV-1 envelope gp120 with the host CD4 receptor is a critical step for viral entry, and therefore the CD4-binding site (CD4bs) of gp120 is a potential hotspot for blocking HIV-1 infection. The present study aimed to design short peptides from well-characterized CD4bs targeting broadly neutralizing antibodies (bNAbs), which could be utilized as bNAb mimetics for viral neutralization. Co-crystallized structures of HIV-1 gp120 in complex with CD4bs-directed bNAbs were used to derive hexameric peptides using the Rosetta Peptiderive protocol. Based on empirical insights into co-crystallized structures, peptides derived from the heavy chain alone were considered. The peptides were docked with both HIV-1 subtype B and C gp120, and the stability of the peptide–antigen complexes was validated using extensive Molecular Dynamics (MD) simulations. Two peptides identified in the study demonstrated stable intermolecular interactions with SER365, GLY366, and GLY367 of the PHE43 cavity in the CD4 binding pocket, and with ASP368 of HIV-1 gp120, thereby mimicking the natural interaction between ASP368gp120 and ARG59CD4–RECEPTOR. Furthermore, the peptides featured favorable physico-chemical properties for virus neutralization suggesting that these peptides may be highly promising bNAb mimetic candidates that may be taken up for experimental validation.
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28
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Fassio AV, Shub L, Ponzoni L, McKinley J, O’Meara MJ, Ferreira RS, Keiser MJ, de Melo Minardi RC. Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. J Chem Inf Model 2022; 62:4300-4318. [DOI: 10.1021/acs.jcim.2c00695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alexandre V. Fassio
- São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo 13563-120, Brazil
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Laura Shub
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Luca Ponzoni
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Jessica McKinley
- Gilead Sciences, Inc., Foster City, California 94404, United States
| | - Matthew J. O’Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Rafaela S. Ferreira
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Michael J. Keiser
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Raquel C. de Melo Minardi
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
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29
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Qu X, Dong L, Zhang J, Si Y, Wang B. Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules. J Chem Inf Model 2022; 62:4369-4379. [PMID: 36083808 DOI: 10.1021/acs.jcim.2c00916] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.
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Affiliation(s)
- Xiaoyang Qu
- 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Jinyan Zhang
- 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
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30
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Yang C, Chen EA, Zhang Y. Protein-Ligand Docking in the Machine-Learning Era. Molecules 2022; 27:4568. [PMID: 35889440 PMCID: PMC9323102 DOI: 10.3390/molecules27144568] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022] Open
Abstract
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Eric Anthony Chen
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, NY 10003, USA; (C.Y.); (E.A.C.)
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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31
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Dong L, Qu X, Wang B. XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking. ACS OMEGA 2022; 7:21727-21735. [PMID: 35785279 PMCID: PMC9245135 DOI: 10.1021/acsomega.2c01723] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Prediction of protein-ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein-ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein-ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein-ligand complexes.
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Affiliation(s)
- Lina Dong
- State
Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry,
iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, P. R. China
| | - Xiaoyang Qu
- 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 360015, 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 360015, P. R. China
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32
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Zheng J, Xiao X, Qiu WR. DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method. Front Genet 2022; 13:859188. [PMID: 35754843 PMCID: PMC9213727 DOI: 10.3389/fgene.2022.859188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/25/2022] [Indexed: 11/20/2022] Open
Abstract
Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.
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Affiliation(s)
- Jie Zheng
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
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33
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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34
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Volkov M, Turk JA, Drizard N, Martin N, Hoffmann B, Gaston-Mathé Y, Rognan D. On the Frustration to Predict Binding Affinities from Protein-Ligand Structures with Deep Neural Networks. J Med Chem 2022; 65:7946-7958. [PMID: 35608179 DOI: 10.1021/acs.jmedchem.2c00487] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in their free and bound states, we unambiguously evidence that an explicit description of protein-ligand noncovalent interactions does not provide any advantage with respect to ligand or protein descriptors. Simple models, inferring binding affinities of test samples from that of the closest ligands or proteins in the training set, already exhibit good performances, suggesting that memorization largely dominates true learning in the deep neural networks. The current study suggests considering only noncovalent interactions while omitting their protein and ligand atomic environments. Removing all hidden biases probably requires much denser protein-ligand training matrices and a coordinated effort of the drug design community to solve the necessary protein-ligand structures.
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Affiliation(s)
- Mikhail Volkov
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, Illkirch 67400, France
| | | | | | | | | | | | - Didier Rognan
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, 74 route du Rhin, Illkirch 67400, France
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35
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Yang C, Zhang Y. Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions. J Chem Inf Model 2022; 62:2696-2712. [PMID: 35579568 DOI: 10.1021/acs.jcim.2c00485] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function ΔLin_F9XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of ΔLin_F9XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of ΔLin_F9XGB in virtual screening application. The ΔLin_F9XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).
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Affiliation(s)
- Chao Yang
- 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.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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Liu X, Feng H, Wu J, Xia K. Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction. PLoS Comput Biol 2022; 18:e1009943. [PMID: 35385478 PMCID: PMC8985993 DOI: 10.1371/journal.pcbi.1009943] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/21/2022] [Indexed: 11/19/2022] Open
Abstract
With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begun to gain momentum recently. AI-based drug design has great promise to revolutionize pharmaceutical industries by significantly reducing the time and cost in drug discovery processes. However, a major issue remains for all AI-based learning model that is efficient molecular representations. Here we propose Dowker complex (DC) based molecular interaction representations and Riemann Zeta function based molecular featurization, for the first time. Molecular interactions between proteins and ligands (or others) are modeled as Dowker complexes. A multiscale representation is generated by using a filtration process, during which a series of DCs are generated at different scales. Combinatorial (Hodge) Laplacian matrices are constructed from these DCs, and the Riemann zeta functions from their spectral information can be used as molecular descriptors. To validate our models, we consider protein-ligand binding affinity prediction. Our DC-based machine learning (DCML) models, in particular, DC-based gradient boosting tree (DC-GBT), are tested on three most-commonly used datasets, i.e., including PDBbind-2007, PDBbind-2013 and PDBbind-2016, and extensively compared with other existing state-of-the-art models. It has been found that our DC-based descriptors can achieve the state-of-the-art results and have better performance than all machine learning models with traditional molecular descriptors. Our Dowker complex based machine learning models can be used in other tasks in AI-based drug design and molecular data analysis. With the ever-increasing accumulation of chemical and biomolecular data, data-driven artificial intelligence (AI) models will usher in an era of faster, cheaper and more-efficient drug design and drug discovery. However, unlike image, text, video, audio data, molecular data from chemistry and biology, have much complicated three-dimensional structures, as well as physical and chemical properties. Efficient molecular representations and descriptors are key to the success of machine learning models in drug design. Here, we propose Dowker complex based molecular representation and Riemann Zeta function based molecular featurization, for the first time. To characterize the complicated molecular structures and interactions at the atomic level, Dowker complexes are constructed. Based on them, intrinsic mathematical invariants are derived and used as molecular descriptors, which can be further combined with machine learning and deep learning models. Our model has achieved state-of-the-art results in protein-ligand binding affinity prediction, demonstrating its great potential for other drug design and discovery problems.
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Affiliation(s)
- Xiang Liu
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
- Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China
- Center for Topology and Geometry Based Technology, Hebei Normal University, Hebei, China
| | - Huitao Feng
- Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China
- Mathematical Science Research Center, Chongqing University of Technology, Chongqing, China
| | - Jie Wu
- Center for Topology and Geometry Based Technology, Hebei Normal University, Hebei, China
- School of Mathematical Sciences, Hebei Normal University, Hebei, China
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
- * E-mail:
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37
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Moon S, Zhung W, Yang S, Lim J, Kim WY. PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chem Sci 2022; 13:3661-3673. [PMID: 35432900 PMCID: PMC8966633 DOI: 10.1039/d1sc06946b] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/06/2022] [Indexed: 12/21/2022] Open
Abstract
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.
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Affiliation(s)
- Seokhyun Moon
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Wonho Zhung
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Soojung Yang
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Jaechang Lim
- HITS Incorporation 124 Teheran-ro, Gangnam-gu Seoul 06234 Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
- HITS Incorporation 124 Teheran-ro, Gangnam-gu Seoul 06234 Republic of Korea
- KI for Artificial Intelligence, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
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38
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Kumar SP, Dixit NY, Patel CN, Rawal RM, Pandya HA. PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments. J Comput Chem 2022; 43:847-863. [PMID: 35301752 DOI: 10.1002/jcc.26840] [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] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/09/2022]
Abstract
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.
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Affiliation(s)
- Sivakumar Prasanth Kumar
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Nandan Y Dixit
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Chirag N Patel
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Rakesh M Rawal
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Himanshu A Pandya
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
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39
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Jiang D, Sun H, Wang J, Hsieh CY, Li Y, Wu Z, Cao D, Wu J, Hou T. Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning. Brief Bioinform 2022; 23:6513729. [DOI: 10.1093/bib/bbab597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/14/2022] Open
Abstract
Abstract
Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering of atom chemical environment, which may consequently introduce domain bias. In this study, SuperAtomicCharge, a data-driven deep graph learning framework, was proposed to predict three important types of partial charges (i.e. RESP, DDEC4 and DDEC78) derived from high-level QM calculations based on the structures of molecules. SuperAtomicCharge was designed to simultaneously exploit the 2D and 3D structural information of molecules, which was proved to be an effective way to improve the prediction accuracy of the model. Moreover, a simple transfer learning strategy and a multitask learning strategy based on self-supervised descriptors were also employed to further improve the prediction accuracy of the proposed model. Compared with the latest baselines, including one GNN-based predictor and two ML-based predictors, SuperAtomicCharge showed better performance on all the three external test sets and had better usability and portability. Furthermore, the QM partial charges of new molecules predicted by SuperAtomicCharge can be efficiently used in drug design applications such as structure-based virtual screening, where the predicted RESP and DDEC4 charges of new molecules showed more robust scoring and screening power than the commonly used partial charges. Finally, two tools including an online server (http://cadd.zju.edu.cn/deepchargepredictor) and the source code command lines (https://github.com/zjujdj/SuperAtomicCharge) were developed for the easy access of the SuperAtomicCharge services.
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40
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Dhakal A, McKay C, Tanner JJ, Cheng J. Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. Brief Bioinform 2022; 23:bbab476. [PMID: 34849575 PMCID: PMC8690157 DOI: 10.1093/bib/bbab476] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/28/2021] [Accepted: 10/15/2021] [Indexed: 12/13/2022] Open
Abstract
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.
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Affiliation(s)
- Ashwin Dhakal
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Cole McKay
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
| | - John J Tanner
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
- Department of Chemistry, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
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41
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Jiang P, Chi Y, Li XS, Liu X, Hua XS, Xia K. Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design. Brief Bioinform 2022; 23:6485012. [PMID: 34958660 DOI: 10.1093/bib/bbab527] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/01/2021] [Accepted: 11/14/2021] [Indexed: 01/05/2023] Open
Abstract
Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neural networks (CNNs) and graph neural networks, effective and efficient representations that characterize the structural, physical, chemical and biological properties of molecular structures and interactions remain to be a great challenge. Here, we propose an equal-sized molecular 2D image representation, known as the molecular persistent spectral image (Mol-PSI), and combine it with CNN model for AI-based drug design. Mol-PSI provides a unique one-to-one image representation for molecular structures and interactions. In general, deep models are empowered to achieve better performance with systematically organized representations in image format. A well-designed parallel CNN architecture for adapting Mol-PSIs is developed for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016, are better than all traditional machine learning models, as far as we know. Our Mol-PSI model provides a powerful molecular representation that can be widely used in AI-based drug design and molecular data analysis.
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Affiliation(s)
- Peiran Jiang
- Drug Discovery Intelligence, AI Center, Alibaba Group DAMO Academy, Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China
| | - Ying Chi
- Drug Discovery Intelligence, AI Center, Alibaba Group DAMO Academy, Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China
| | - Xiao-Shuang Li
- Drug Discovery Intelligence, AI Center, Alibaba Group DAMO Academy, Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China
| | - Xiang Liu
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
- Chern Institute of Mathematics and LPMC, Nankai University, 300071, Tianjin, China
| | - Xian-Sheng Hua
- Drug Discovery Intelligence, AI Center, Alibaba Group DAMO Academy, Wen Yi Xi Road, Yuhang District, Hangzhou City , 310000, Zhejiang, China
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
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42
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Abstract
Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.
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Affiliation(s)
- Chao Yang
- 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
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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43
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Xue LY, Guo F, Wen YS, Feng SQ, Huang XN, Guo L, Li HS, Cui SX, Zhang GQ, Wang QL. ReaxFF-MPNN machine learning potential: a combination of reactive force field and message passing neural networks. Phys Chem Chem Phys 2021; 23:19457-19464. [PMID: 34524283 DOI: 10.1039/d1cp01656c] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Reactive force field (ReaxFF) is a powerful computational tool for exploring material properties. In this work, we proposed an enhanced reactive force field model, which uses message passing neural networks (MPNN) to compute the bond order and bond energies. MPNN are a variation of graph neural networks (GNN), which are derived from graph theory. In MPNN or GNN, molecular structures are treated as a graph and atoms and chemical bonds are represented by nodes and edges. The edge states correspond to the bond order in ReaxFF and are updated by message functions according to the message passing algorithms. The results are very encouraging; the investigation of the potential, such as the potential energy surface, reaction energies and equation of state, are greatly improved by this simple improvement. The new potential model, called reactive force field with message passing neural networks (ReaxFF-MPNN), is provided as an interface in an atomic simulation environment (ASE) with which the original ReaxFF and ReaxFF-MPNN potential models can do MD simulations and geometry optimizations within the ASE. Furthermore, machine learning, based on an active learning algorithm and gradient optimizer, is designed to train the model. We found that the active learning machine not only saves the manual work to collect the training data but is also much more effective than the general optimizer.
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Affiliation(s)
- Li-Yuan Xue
- Shandong Provincial Key Laboratory of Optical Communication Science and Technology, Liaocheng, 252000, China.
| | - Feng Guo
- Shandong Provincial Key Laboratory of Optical Communication Science and Technology, Liaocheng, 252000, China. .,School of Physical Science and Information Technology, Liaocheng University, Liaocheng, 252000, China
| | - Yu-Shi Wen
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), Mianyang, Sichuan, 621900, China.
| | - Shi-Quan Feng
- School of Physics and Electronic Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Xiao-Na Huang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei, 430072, China
| | - Lei Guo
- School of Business, Shandong Normal University, Jinan, 250014, China
| | - Heng-Shuai Li
- Shandong Provincial Key Laboratory of Optical Communication Science and Technology, Liaocheng, 252000, China.
| | - Shou-Xin Cui
- School of Physical Science and Information Technology, Liaocheng University, Liaocheng, 252000, China
| | - Gui-Qing Zhang
- School of Physical Science and Information Technology, Liaocheng University, Liaocheng, 252000, China
| | - Qing-Lin Wang
- Shandong Provincial Key Laboratory of Optical Communication Science and Technology, Liaocheng, 252000, China. .,School of Physical Science and Information Technology, Liaocheng University, Liaocheng, 252000, China
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44
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Kadukova M, Machado KDS, Chacón P, Grudinin S. KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions. Bioinformatics 2021; 37:943-950. [PMID: 32840574 DOI: 10.1093/bioinformatics/btaa748] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/27/2020] [Accepted: 08/18/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Despite the progress made in studying protein-ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations. RESULTS Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction. AVAILABILITYAND IMPLEMENTATION The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https://team.inria.fr/nano-d/korp-pl/ and https://chaconlab.org/modeling/korp-pl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maria Kadukova
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.,Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudniy, Russia
| | - Karina Dos Santos Machado
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.,Computational Biology Laboratory, Centro de Ciências Computacionais, Universidade Federal do Rio Grande - FURG, Rio Grande, RS 96201-090, Brazil
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid 28006, Spain
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France
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45
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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46
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Computational study of the binding mode, action mechanism and potency of pregabalin through molecular docking and quantum mechanical descriptors. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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47
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Wang S, Shan P, Zhao Y, Zuo L. GanDTI: A multi-task neural network for drug-target interaction prediction. Comput Biol Chem 2021; 92:107476. [PMID: 33799080 DOI: 10.1016/j.compbiolchem.2021.107476] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/08/2021] [Accepted: 03/09/2021] [Indexed: 11/18/2022]
Abstract
Drug discovery processes require drug-target interaction (DTI) prediction by virtual screenings with high accuracy. Compared with traditional methods, the deep learning method requires less time and domain expertise, while achieving higher accuracy. However, there is still room for improvement for higher performance with simplified structures. Meanwhile, this field is calling for multi-task models to solve different tasks. Here we report the GanDTI, an end-to-end deep learning model for both interaction classification and binding affinity prediction tasks. This model employs the compound graph and protein sequence data. It only consists of a graph neural network, an attention module and a multiple-layer perceptron, yet outperforms the state-of-the art methods to predict binding affinity and interaction classification on the DUD-E, human, and bindingDB benchmark datasets. This demonstrates our refined model is highly effective and efficient for DTI prediction and provides a new strategy for performance improvement.
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Affiliation(s)
- Shuyu Wang
- Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei, 066001, PR China.
| | - Peng Shan
- Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei, 066001, PR China
| | - Yuliang Zhao
- Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei, 066001, PR China
| | - Lei Zuo
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
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48
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Lim S, Lu Y, Cho CY, Sung I, Kim J, Kim Y, Park S, Kim S. A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput Struct Biotechnol J 2021; 19:1541-1556. [PMID: 33841755 PMCID: PMC8008185 DOI: 10.1016/j.csbj.2021.03.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 01/27/2023] Open
Abstract
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
| | - Yijingxiu Lu
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Chang Yun Cho
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Inyoung Sung
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Jungwoo Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Youngkuk Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
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49
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Sakai M, Nagayasu K, Shibui N, Andoh C, Takayama K, Shirakawa H, Kaneko S. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci Rep 2021; 11:525. [PMID: 33436854 PMCID: PMC7803991 DOI: 10.1038/s41598-020-80113-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/17/2020] [Indexed: 01/29/2023] Open
Abstract
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.
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Affiliation(s)
- Miyuki Sakai
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan ,Medical Database Ltd., 2-5-5 Sumitomoshibadaimon building, Shibadaimon, Minato-ku, Tokyo, 105-0012 Japan
| | - Kazuki Nagayasu
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Norihiro Shibui
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Chihiro Andoh
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Kaito Takayama
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Hisashi Shirakawa
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Shuji Kaneko
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
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