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Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM\/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.<\/jats:p>","DOI":"10.1093\/bib\/bbad008","type":"journal-article","created":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T14:10:48Z","timestamp":1674396648000},"source":"Crossref","is-referenced-by-count":36,"title":["Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?"],"prefix":"10.1093","volume":"24","author":[{"given":"Shukai","family":"Gu","sequence":"first","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2783-5529","authenticated-orcid":false,"given":"Chao","family":"Shen","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"given":"Jiahui","family":"Yu","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"given":"Hong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-3667","authenticated-orcid":false,"given":"Huanxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Macao Polytechnic University Faculty of Applied Science, , Macao, SAR , China"}]},{"given":"Liwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd. Advanced Computing and Storage Laboratory, , Shenzhen 518129, Guangdong , China"}]},{"given":"Rong","family":"Sheng","sequence":"additional","affiliation":[{"name":"Huawei Device Co., Ltd. Health Technology Development Dept, , Dongguan 523808, Guangdong , China"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology , Changzhou 213001 , China"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-2580","authenticated-orcid":false,"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]},{"given":"Yu","family":"Kang","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, Zhejiang , China"}]}],"member":"286","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","article-title":"Principles of early drug discovery","volume":"162","author":"Hughes","year":"2011","journal-title":"Br J Pharmacol"},{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1124\/pr.112.007336","article-title":"Computational methods in drug discovery","volume":"66","author":"Sliwoski","year":"2014","journal-title":"Pharmacol Rev"},{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1021\/acs.jcim.0c01306","article-title":"Improved protein-ligand binding affinity prediction with structure-based deep fusion inference","volume":"61","author":"Jones","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"e1429","DOI":"10.1002\/wcms.1429","article-title":"From machine learning to deep learning: advances in scoring functions for protein-ligand docking","volume":"10","author":"Shen","year":"2020","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1517\/17460441.2015.1032936","article-title":"The MM\/PBSA and MM\/GBSA methods to estimate ligand-binding affinities","volume":"10","author":"Genheden","year":"2015","journal-title":"Expert Opin Drug Discov"},{"key":"2023032004361883500_","doi-asserted-by":"crossref","first-page":"18958","DOI":"10.1039\/C9CP04096J","article-title":"Assessing the performance of the MM\/PBSA and MM\/GBSA methods. 10. 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