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Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit\u2013cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https:\/\/github.com\/DeltaGroupNJUPT\/Vina-GPU-2.0, coupled with explicit instructions and examples.<\/jats:p>","DOI":"10.1021\/acs.jcim.2c01504","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T23:53:38Z","timestamp":1679356418000},"page":"1982-1998","source":"Crossref","is-referenced-by-count":125,"title":["Vina-GPU 2.0: Further Accelerating AutoDock Vina and\nIts Derivatives with Graphics Processing Units"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7787-6989","authenticated-orcid":true,"given":"Ji","family":"Ding","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210023"]},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province , ,","place":["Nanjing, China, 210023"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-7411","authenticated-orcid":true,"given":"Shidi","family":"Tang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210023"]},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province , ,","place":["Nanjing, China, 210023"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheming","family":"Mei","sequence":"additional","affiliation":[{"name":"University of Toronto , , ,","place":["Toronto, Canada, M5S 1A4"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210003"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinqin","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210003"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6585-3106","authenticated-orcid":true,"given":"Haifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210003"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Ling","sequence":"additional","affiliation":[{"name":"Southeast University , , ,","place":["Nanjing, China, 210096"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7941-9722","authenticated-orcid":true,"given":"Jiansheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications , , ,","place":["Nanjing, China, 210023"]},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province , ,","place":["Nanjing, China, 210023"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"2026081804441758200_cit1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/(SICI)1098-1128(199601)16:1&lt;3::AID-MED1&gt;3.0.CO;2-6","article-title":"The art\nand practice of structure-based\ndrug design: a molecular modeling perspective","volume":"16","author":"Bohacek","year":"1996","journal-title":"Medicinal research reviews"},{"key":"2026081804441758200_cit2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","article-title":"Ultra-large library\ndocking for discovering\nnew chemotypes","volume":"566","author":"Lyu","year":"2019","journal-title":"Nature"},{"key":"2026081804441758200_cit3","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: improving\nthe speed and accuracy of\ndocking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. 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