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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2020

Baseline Model for Predicting Protein–Ligand Unbinding Kinetics through Machine Learning

Nurlybek Amangeldiuly, Dmitry S. Karlov, Maxim V. Fedorov

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Baseline Model for Predicting Protein–Ligand Unbinding Kinetics through Machine LearningOriginalPrediction of Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis0FVM Network to Reduce Computational Cost of CFD Simulation.3Prediction of the Drug–Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis25Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions256The prediction of protein–ligand unbinding for modern drug discovery25Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows42Public Data Set of Protein–Ligand Dissociation Kinetic Constants for Quantitative Structure–Kinetics Relationship Studies27Machine Learning Methods for Molecular Dynamic Simulations0The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods131Structure-Kinetic Relationship for Drug Design Revealed by PLS Model with Retrosynthesis-Based Pre-trained Molecular Representation and Molecular Dynamics Simulation1Advances in computational methods for ligand binding kinetics64Identification of InhA-Inhibitors Interaction Fingerprints that Affect Residence Time0Machine learning methods in drug design5The importance of binding kinetics and drug–target residence time in pharmacology77Structure–Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation5Machine learning methods for developments of binding kinetic models in predicting protein‐ligand dissociation rate constants30Molecular simulation for food protein–ligand interactions: A comprehensive review on principles, current applications, and emerging trends83Hybrid deep learning model for evaluations of protein-ligand binding kinetic property1Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery38Using Dynamic Interaction Fingerprints to Derive Baseline Machine Learning Model for the Prediction of Protein-Ligand Dissociation Rate Constant3Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies22Deciphering the Selectivity of CBL-B Inhibitors Using All-Atom Molecular Dynamics and Machine Learning3Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time7DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model1Binding Affinity Is Not Binding Behavior: Reframing Learned Molecular Recognition around Kinetics, Conformational Change, and Residence Time0High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation10DCBK: A Fragment-Based Hybrid Simulation and Machine Learning Framework for Predicting Ligand Dissociation Kinetics0A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction0BiCoA-Net: An Interpretable Bidirectional Co-Attention Framework for Predicting Protein-Ligand Binding Kinetics1Label-Free, AI-Driven Evanescent Microscopy Decodes Single-Cell Membrane Protein Binding Kinetics and Adhesion Biomechanics0Prediction accuracy of ligand binding kinetics1Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction2PySTARC : GPU-accelerated Brownian dynamics for bimolecular association rate constants0

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  1. Prediction of Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

    ChemRxiv · 2021-03-17

    Ariane Nunes‐Alves, Fabian Ormersbach, Rebecca C. Wade

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  2. FVM Network to Reduce Computational Cost of CFD Simulation.

    arXiv (Cornell University) · 2021-05-07

    Joongoo Jeon, Sung Joong Kim

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  3. Prediction of the Drug–Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

    Journal of Chemical Information and Modeling · 2021-07-01

    Ariane Nunes‐Alves, Fabian Ormersbach, Rebecca C. Wade, Ariane Nunes‐Alves

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  4. Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

    Briefings in Bioinformatics · 2021-10-19

    Ashwin Dhakal, Cole McKay, John J. Tanner, Jianlin Jack Cheng

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  5. The prediction of protein–ligand unbinding for modern drug discovery

    Expert Opinion on Drug Discovery · 2021-11-03

    Qianqian Zhang, Nannan Zhao, Xiaoxiao Meng, Fansen Yu, Xiaojun Yao, Huanxiang Liu

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  6. Finite volume method network for the acceleration of unsteady computational fluid dynamics: Non‐reacting and reacting flows

    International Journal of Energy Research · 2022-03-29

    Joongoo Jeon, Juhyeong Lee, Sung Joong Kim

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  7. Public Data Set of Protein–Ligand Dissociation Kinetic Constants for Quantitative Structure–Kinetics Relationship Studies

    ACS Omega · 2022-05-26

    Huisi Liu, Minyi Su, Hai‐Xia Lin, Renxiao Wang, Yan Li

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  8. Machine Learning Methods for Molecular Dynamic Simulations

    Unknown journal · 2022-07-06

    Zineb Belkacemi

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  9. The (Re)-Evolution of Quantitative Structure–Activity Relationship (QSAR) Studies Propelled by the Surge of Machine Learning Methods

    Journal of Chemical Information and Modeling · 2022-11-28

    Thereza A. Soares, Ariane Nunes‐Alves, Angelica Mazzolari, Fiorella Ruggiu, Guo‐Wei Wei, Kenneth M. Merz

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  10. Structure-Kinetic Relationship for Drug Design Revealed by PLS Model with Retrosynthesis-Based Pre-trained Molecular Representation and Molecular Dynamics Simulation

    bioRxiv (Cold Spring Harbor Laboratory) · 2022-11-29

    Feng Zhou, Shiqiu Yin, Yi Xiao, Zaiyun Lin, Weiqiang Fu, Yingsheng Zhang

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  11. Advances in computational methods for ligand binding kinetics

    Trends in Biochemical Sciences · 2022-12-22

    Farzin Sohraby, Ariane Nunes‐Alves

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  12. Identification of InhA-Inhibitors Interaction Fingerprints that Affect Residence Time

    Lecture notes in computer science · 2023-01-01

    Magdalena Ługowska, Marcin Pacholczyk

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  13. Machine learning methods in drug design

    Elsevier eBooks · 2023-01-01

    Gabriel Corrêa Veríssimo, Jadson Castro Gertrudes, Vinícius Gonçalves Maltarollo

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  14. The importance of binding kinetics and drug–target residence time in pharmacology

    British Journal of Pharmacology · 2023-05-10

    Kevin E. Knockenhauer, Robert A. Copeland

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  15. Structure–Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

    ACS Omega · 2023-05-12

    Feng Zhou, Shiqiu Yin, Yi Xiao, Zaiyun Lin, Weiqiang Fu, Yingsheng Zhang

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  16. Machine learning methods for developments of binding kinetic models in predicting protein‐ligand dissociation rate constants

    Smart Molecules · 2023-11-10

    Yujing Zhao, Qilei Liu, Jian Du, Qingwei Meng, Lei Zhang

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  17. Molecular simulation for food protein–ligand interactions: A comprehensive review on principles, current applications, and emerging trends

    Comprehensive Reviews in Food Science and Food Safety · 2023-12-13

    Zihan Jin, Zihao Wei

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  18. Hybrid deep learning model for evaluations of protein-ligand binding kinetic property

    Computer-aided chemical engineering/Computer aided chemical engineering · 2024-01-01

    Yujing Zhao, Qilei Liu, Yu Zhuang, Yachao Dong, Linlin Liu, Linlin Liu, Jian Du, Qingwei Meng, Lei Zhang

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  19. Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

    Biomolecules · 2024-01-05

    Zoe Li, Ruili Huang, Menghang Xia, Tucker A. Patterson, Huixiao Hong

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  20. Using Dynamic Interaction Fingerprints to Derive Baseline Machine Learning Model for the Prediction of Protein-Ligand Dissociation Rate Constant

    ChemRxiv · 2024-03-07

    Muhammad Jan Akhunzada, Hyun Jung Yoon, A. Braka, Indrajit Deb, Sangwook Wu

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  21. Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies

    JACS Au · 2024-04-01

    Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, Fang Bai

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  22. Deciphering the Selectivity of CBL-B Inhibitors Using All-Atom Molecular Dynamics and Machine Learning

    ACS Medicinal Chemistry Letters · 2024-06-05

    Feng Zhou, Haolin Du, Yang Wang, Weiqiang Fu, Bingchen Zhao, Jielong Zhou, Yingsheng Zhang

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  23. Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time

    Journal of Chemical Information and Modeling · 2024-11-04

    Yujing Zhao, Lei Zhang, Jian Du, Qingwei Meng, Li Zhang, Heshuang Wang, Liang Sun, Qilei Liu

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  24. DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model

    IEEE Transactions on Computational Biology and Bioinformatics · 2024-11-07

    Orhun Vural, Leon Jololian, Lurong Pan

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  25. Binding Affinity Is Not Binding Behavior: Reframing Learned Molecular Recognition around Kinetics, Conformational Change, and Residence Time

    Pharmacophore · 2025-01-01

    Binta Jallow, M L Conteh, Christopher Okafor, Fatou Sanyang

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  26. High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation

    Journal of Chemical Theory and Computation · 2025-04-26

    Wenbo Yu, Shashi Kumar, Mingtian Zhao, David J. Weber, Alexander D. MacKerell

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  27. DCBK: A Fragment-Based Hybrid Simulation and Machine Learning Framework for Predicting Ligand Dissociation Kinetics

    bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-01

    Yang Li, Yixin Wang, Xueying Zheng, Jinjiang Guo, Rumin Zhang

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  28. A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction

    Cell Reports Methods · 2026-02-01

    Chang Sun, Zichen Qin, Minglei Li, Yanfei Li, Rong Tang, Shengquan Chen, Yuxiang Wang, Yanqiang Liu, Jinmao Wei, Jiayi Liu

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  29. BiCoA-Net: An Interpretable Bidirectional Co-Attention Framework for Predicting Protein-Ligand Binding Kinetics

    Acta Physico-Chimica Sinica · 2026-03-01

    Jing Li, Yuwei Yang, Shukai Gu, Shihang Wang, Bo Liu, Huanxiang Liu, Huanxiang Liu, Xiaojun Yao

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  30. Label-Free, AI-Driven Evanescent Microscopy Decodes Single-Cell Membrane Protein Binding Kinetics and Adhesion Biomechanics

    Analytical Chemistry · 2026-03-12

    Caixin Huang, Bo Zhang, Zhaoyang Liu, Mengdi Wang, Liangju Li, Jiying Xu, Yi Chen, Ying Zhao, Pengfei Zhang

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  31. Prediction accuracy of ligand binding kinetics

    Biophysical Journal · 2026-03-18

    Fu Xiao, Yinglong Miao

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  32. Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction

    Journal of Cheminformatics · 2026-05-20

    Shuo Zhang, Li Xie, Li Xie, Daniel Tiourine, Lei Xie, Lei Xie

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  33. PySTARC : GPU-accelerated Brownian dynamics for bimolecular association rate constants

    bioRxiv (Cold Spring Harbor Laboratory) · 2026-09-29

    Anupam Anand Ojha, Gary Huber, Shiksha Dutta, Sonya M. Hanson

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