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Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was\u00a0proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all\u00a0these docking programs, there is\u00a0still large room for performance improvement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein\u2013ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein\u2013ligand docking and virtual screening. Owing to the existing coevolution between the\u00a0master and the\u00a0slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/li-jin-xing\/MPSOVina\">https:\/\/github.com\/li-jin-xing\/MPSOVina<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-04711-0","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T11:03:54Z","timestamp":1653908634000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Parallel multi-swarm cooperative particle swarm optimization for protein\u2013ligand docking and virtual screening"],"prefix":"10.1186","volume":"23","author":[{"given":"Chao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasile","family":"Palade","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bilal","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"issue":"5","key":"4711_CR1","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1038\/nprot.2016.051","volume":"11","author":"S Forli","year":"2016","unstructured":"Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. 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