DeepRLI: a multi-objective framework for universal protein–ligand interaction prediction

Haoyu Lin, Jintao Zhu, Shiwei Wang, Yibo Li, Jianfeng Pei and Luhua Lai

Digital Discovery, 2025, 4, 2083
DOI: 10.1039/D4DD00403E

CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training

Heng Cai, Chao Shen, Tianye Jian, Xujun Zhang, Tong Chen, Xiaoqi Han, Zhuo Yang, Wei Dang, Chang-Yu Hsieh, Yu Kang, Peichen Pan, Xiangyang Ji, Jianfei Song, Tingjun Hou and Yafeng Deng

Chem. Sci., 2024, 15, 1449
DOI: 10.1039/D3SC05552C

Robust protein–ligand interaction modeling through integrating physical laws and geometric knowledge for absolute binding free energy calculation

Qun Su, Jike Wang, Qiaolin Gou, Renling Hu, Linlong Jiang, Hui Zhang, Tianyue Wang, Yifei Liu, Chao Shen, Yu Kang, Chang-Yu Hsieh and Tingjun Hou

Chem. Sci., 2025, 16, 5043
DOI: 10.1039/D4SC07405J

PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening

Seonghwan Seo and Woo Youn Kim

Chem. Sci., 2024, 15, 19473
DOI: 10.1039/D4SC04854G

Increase Docking Score Screening Power by Simple Fusion With CNNscore

Huicong Liang, Aowei Xie, Ning Hou, Fengjiao Wei, Ting Gao, Jiajie Li, Xinru Gao, Chuanqin Shi, Gaokeng Xiao and Ximing Xu

J Comput Chem, 2025, 46
DOI: 10.1002/jcc.70060

Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era

Jingyi Dai, Ziyi Zhou, Yanru Zhao, Fanjing Kong, Zhenwei Zhai, Zhishan Zhu, Jie Cai, Sha Huang, Ying Xu and Tao Sun

European Journal of Medicinal Chemistry, 2025, 283, 117162
DOI: 10.1016/j.ejmech.2024.117162

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy

Saber Imani, Xiaoyan Li, Keyi Chen, Mazaher Maghsoudloo, Parham Jabbarzadeh Kaboli, Mehrdad Hashemi, Saloomeh Khoushab and Xiaoping Li

Front. Cell. Infect. Microbiol., 2025, 14
DOI: 10.3389/fcimb.2024.1501010

SCORCH2: A Generalized Heterogeneous Consensus Model for High‐Enrichment Interaction‐Based Virtual Screening

Lin Chen, Vincent Blay, Pedro J. Ballester and Douglas R. Houston

Advanced Science, 2025
DOI: 10.1002/advs.202508318

Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review

Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah and Kaliyaperumal Raja

J Bio-X Res, 2025, 8
DOI: 10.34133/jbioxresearch.0041

Integrated Molecular Modeling and Machine Learning for Drug Design

Song Xia, Eric Chen and Yingkai Zhang

J. Chem. Theory Comput., 2023, 19, 7478
DOI: 10.1021/acs.jctc.3c00814

Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Ding Luo, Dandan Liu, Xiaoyang Qu, Lina Dong and Binju Wang

J. Chem. Inf. Model., 2024, 64, 1892
DOI: 10.1021/acs.jcim.3c01961

Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions

Dong Chen, Jian Liu and Guo-Wei Wei

Nat Mach Intell, 2024, 6, 799
DOI: 10.1038/s42256-024-00855-1

Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity

Jiashan Li and Xinqi Gong

BMC Bioinformatics, 2025, 26
DOI: 10.1186/s12859-025-06064-w

A new paradigm for applying deep learning to protein–ligand interaction prediction

Zechen Wang, Sheng Wang, Yangyang Li, Jingjing Guo, Yanjie Wei, Yuguang Mu, Liangzhen Zheng and Weifeng Li

Briefings in Bioinformatics, 2024, 25
DOI: 10.1093/bib/bbae145

A Multi‐Objective Molecular Generation Method Based on Pareto Algorithm and Monte Carlo Tree Search

Yifei Liu, Yiheng Zhu, Jike Wang, Renling Hu, Chao Shen, Wanglin Qu, Gaoang Wang, Qun Su, Yuchen Zhu, Yu Kang, Peichen Pan, Chang‐Yu Hsieh and Tingjun Hou

Advanced Science, 2025, 12
DOI: 10.1002/advs.202410640

Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks

Yiyu Hong, Junsu Ha, Jaemin Sim, Chae Jo Lim, Kwang-Seok Oh, Ramakrishnan Chandrasekaran, Bomin Kim, Jieun Choi, Junsu Ko, Woong-Hee Shin and Juyong Lee

J Cheminform, 2024, 16
DOI: 10.1186/s13321-024-00912-2

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies

Seokhyun Moon, Wonho Zhung and Woo Youn Kim

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1705

Virtual Screening Technique to Identify Inhibitors of Mycobacterium tuberculosis Rv3032 Protein Involved in MGLP Biosynthesis

Lavanya Rumandla, Mounika Badineni, Ramesh Malikanti, Rajender Vadija, Kiran Kumar Mustyala and Vasavi Malkhed

Russ J Bioorg Chem, 2024, 50, 1067
DOI: 10.1134/S1068162024030300

BoostSF-SHAP: Gradient boosting-based software for protein–ligand binding affinity prediction with explanations

Xingqian Chen, Shuangbao Song, Zhenyu Song, Shuangyu Song and Junkai Ji

Neurocomputing, 2025, 622, 129303
DOI: 10.1016/j.neucom.2024.129303

Improving the Reliability of Language Model-Predicted Structures as Docking Targets through Geometric Graph Learning

Chao Shen, Xiaoqi Han, Heng Cai, Tong Chen, Yu Kang, Peichen Pan, Xiangyang Ji, Chang-Yu Hsieh, Yafeng Deng and Tingjun Hou

J. Med. Chem., 2025, 68, 1956
DOI: 10.1021/acs.jmedchem.4c02740

Benchmarking AI-powered docking methods from the perspective of virtual screening

Shukai Gu, Chao Shen, Xujun Zhang, Huiyong Sun, Heng Cai, Hao Luo, Huifeng Zhao, Bo Liu, Hongyan Du, Yihao Zhao, Chenggong Fu, Silong Zhai, Yafeng Deng, Huanxiang Liu, Tingjun Hou and Yu Kang

Nat Mach Intell, 2025, 7, 509
DOI: 10.1038/s42256-025-00993-0

Protein language models are performant in structure-free virtual screening

Hilbert Yuen In Lam, Jia Sheng Guan, Xing Er Ong, Robbe Pincket and Yuguang Mu

Briefings in Bioinformatics, 2024, 25
DOI: 10.1093/bib/bbae480

Normalized Protein–Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening

Song Xia, Yaowen Gu and Yingkai Zhang

J. Chem. Inf. Model., 2025, 65, 1101
DOI: 10.1021/acs.jcim.4c01014

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks

Pierre-Yves Libouban, Samia Aci-Sèche, Jose Carlos Gómez-Tamayo, Gary Tresadern and Pascal Bonnet

IJMS, 2023, 24, 16120
DOI: 10.3390/ijms242216120

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

Tobias Harren, Torben Gutermuth, Christoph Grebner, Gerhard Hessler and Matthias Rarey

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1716

Harnessing deep learning for enhanced ligand docking

Xujun Zhang, Chao Shen, Chang-Yu Hsieh and Tingjun Hou

Trends in Pharmacological Sciences, 2024, 45, 103
DOI: 10.1016/j.tips.2023.12.004

The future of machine learning for small-molecule drug discovery will be driven by data

Guy Durant, Fergus Boyles, Kristian Birchall and Charlotte M. Deane

Nat Comput Sci, 2024, 4, 735
DOI: 10.1038/s43588-024-00699-0

OpenDock: a pytorch-based open-source framework for protein–ligand docking and modelling

Qiuyue Hu, Zechen Wang, Jintao Meng, Weifeng Li, Jingjing Guo, Yuguang Mu, Sheng Wang, Liangzhen Zheng, Yanjie Wei and Jianlin Cheng

Bioinformatics, 2024, 40
DOI: 10.1093/bioinformatics/btae628

CarsiDock-Cov: A deep learning-guided approach for automated covalent docking and screening

Chao Shen, Hongyan Du, Xujun Zhang, Shukai Gu, Heng Cai, Yu Kang, Peichen Pan, Qingwei Zhao and Tingjun Hou

Acta Pharmaceutica Sinica B, 2025
DOI: 10.1016/j.apsb.2025.07.043

Artificial intelligence in drug development

Kang Zhang, Xin Yang, Yifei Wang, Yunfang Yu, Niu Huang, Gen Li, Xiaokun Li, Joseph C. Wu and Shengyong Yang

Nat Med, 2025, 31, 45
DOI: 10.1038/s41591-024-03434-4

Navigating the complexity of p53-DNA binding: implications for cancer therapy

Kelly M. Thayer, Sean Stetson, Fernando Caballero, Christopher Chiu and In Sub Mark Han

Biophys Rev, 2024
DOI: 10.1007/s12551-024-01207-4

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening

Xujun Zhang, Chao Shen, Haotian Zhang, Yu Kang, Chang-Yu Hsieh and Tingjun Hou

Acc. Chem. Res., 2024, 57, 1500
DOI: 10.1021/acs.accounts.4c00093

Recent advances in AI-driven protein-ligand interaction predictions

Jaemin Sim, Dongwoo Kim, Bomin Kim, Jieun Choi and Juyong Lee

Current Opinion in Structural Biology, 2025, 92, 103020
DOI: 10.1016/j.sbi.2025.103020

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery

Chao Shen, Jianfei Song, Chang-Yu Hsieh, Dongsheng Cao, Yu Kang, Wenling Ye, Zhenxing Wu, Jike Wang, Odin Zhang, Xujun Zhang, Hao Zeng, Heng Cai, Yu Chen, Linkang Chen, Hao Luo, Xinda Zhao, Tianye Jian, Tong Chen, Dejun Jiang, Mingyang Wang, Qing Ye, Jialu Wu, Hongyan Du, Hui Shi, Yafeng Deng and Tingjun Hou

J. Chem. Inf. Model., 2024, 64, 5381
DOI: 10.1021/acs.jcim.4c00621