{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:57:53Z","timestamp":1791050273567,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872198"],"award-info":[{"award-number":["61872198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81771478"],"award-info":[{"award-number":["81771478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971216"],"award-info":[{"award-number":["61971216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BK20201378"],"award-info":[{"award-number":["BK20201378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Program of Science and Technology Department of Jiangsu Province","award":["61872198"],"award-info":[{"award-number":["61872198"]}]},{"name":"Basic Research Program of Science and Technology Department of Jiangsu Province","award":["81771478"],"award-info":[{"award-number":["81771478"]}]},{"name":"Basic Research Program of Science and Technology Department of Jiangsu Province","award":["61971216"],"award-info":[{"award-number":["61971216"]}]},{"name":"Basic Research Program of Science and Technology Department of Jiangsu Province","award":["BK20201378"],"award-info":[{"award-number":["BK20201378"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.<\/jats:p>","DOI":"10.3390\/molecules27093041","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:30:28Z","timestamp":1652142628000},"page":"3041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Accelerating AutoDock Vina with GPUs"],"prefix":"10.3390","volume":"27","author":[{"given":"Shidi","family":"Tang","sequence":"first","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6837-5675","authenticated-orcid":false,"given":"Ruiqi","family":"Chen","sequence":"additional","affiliation":[{"name":"VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengru","family":"Lin","sequence":"additional","affiliation":[{"name":"VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingde","family":"Lin","sequence":"additional","affiliation":[{"name":"National ASIC System Engineering Technology Research Center, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5014-3419","authenticated-orcid":false,"given":"Yanxiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7787-6989","authenticated-orcid":false,"given":"Ji","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-7189","authenticated-orcid":false,"given":"Ming","family":"Ling","sequence":"additional","affiliation":[{"name":"National ASIC System Engineering Technology Research Center, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7941-9722","authenticated-orcid":false,"given":"Jiansheng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"146","DOI":"10.2174\/157340911795677602","article-title":"Molecular docking: A powerful approach for structure-based drug discovery","volume":"7","author":"Meng","year":"2011","journal-title":"Curr. Comput.-Aided Drug Des."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/S0959-440X(96)80061-3","article-title":"Computational methods for biomolecular docking","volume":"6","author":"Lengauer","year":"1996","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1021\/jm4004285","article-title":"QSAR modeling: Where have you been? Where are you going to?","volume":"57","author":"Cherkasov","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1023\/A:1025386326946","article-title":"Rational selection of training and test sets for the development of validated QSAR models","volume":"17","author":"Golbraikh","year":"2003","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1021\/acs.jcim.1c00203","article-title":"AutoDock Vina 1.2. 0: New docking methods, expanded force field, and Python bindings","volume":"61","author":"Eberhardt","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ravindranath, P.A., Forli, S., Goodsell, D.S., Olson, A.J., and Sanner, M.F. (2015). AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput. Biol., 11.","DOI":"10.1371\/journal.pcbi.1004586"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5121","DOI":"10.1093\/bioinformatics\/btz459","article-title":"AutoDock CrankPep: Combining folding and docking to predict protein\u2014Peptide complexes","volume":"35","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5161","DOI":"10.1021\/acs.jctc.9b00557","article-title":"Docking flexible cyclic peptides with AutoDock CrankPep","volume":"15","author":"Zhang","year":"2019","journal-title":"J. Chem. Theory Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s10822-019-00241-9","article-title":"D3R Grand Challenge 4: Prospective pose prediction of BACE1 ligands with AutoDock-GPU","volume":"33","author":"Eberhardt","year":"2019","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1021\/acs.jctc.0c01006","article-title":"Accelerating AutoDock4 with GPUs and gradient-based local search","volume":"17","author":"Tillack","year":"2021","journal-title":"J. Chem. Theory Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/pro.3934","article-title":"The AutoDock suite at 30","volume":"30","author":"Goodsell","year":"2021","journal-title":"Protein Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","article-title":"Comparative assessment of scoring functions: The CASF-2016 update","volume":"59","author":"Su","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12964","DOI":"10.1039\/C6CP01555G","article-title":"Comprehensive evaluation of ten docking programs on a diverse set of protein\u2013ligand complexes: The prediction accuracy of sampling power and scoring power","volume":"18","author":"Wang","year":"2016","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_16","unstructured":"Fletcher, R. (2013). Practical Methods of Optimization, John Wiley & Sons."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1063\/1.1699114","article-title":"Equation of state calculations by fast computing machines","volume":"21","author":"Metropolis","year":"1953","journal-title":"J. Chem. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6","article-title":"The art and practice of structure-based drug design: A molecular modeling perspective","volume":"16","author":"Bohacek","year":"1996","journal-title":"Med. Res. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1038\/s41586-019-0917-9","article-title":"Ultra-large library docking for discovering new chemotypes","volume":"566","author":"Lyu","year":"2019","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1038\/s41586-020-2117-z","article-title":"An open-source drug discovery platform enables ultra-large virtual screens","volume":"580","author":"Gorgulla","year":"2020","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, H., Leung, K.S., and Wong, M.H. (2012, January 9\u201312). idock: A multithreaded virtual screening tool for flexible ligand docking. Proceedings of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), San Diego, CA, USA.","DOI":"10.1109\/CIBCB.2012.6217214"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10822-016-9900-9","article-title":"1001 Ways to run AutoDock Vina for virtual screening","volume":"30","author":"Jaghoori","year":"2016","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1002\/jcc.25187","article-title":"Fast and flexible gpu accelerated binding free energy calculations within the amber molecular dynamics package","volume":"39","author":"Mermelstein","year":"2018","journal-title":"J. Comput. Chem."},{"key":"ref_24","unstructured":"Hwu, W.M.W. (2011). GPU Computing Gems Emerald Edition, Morgan Kaufmann Publishers Inc."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stone, J.E., Hynninen, A.P., Phillips, J.C., and Schulten, K. (2016, January 19\u201323). Early experiences porting the NAMD and VMD molecular simulation and analysis software to GPU-accelerated OpenPOWER platforms. Proceedings of the International Conference on High Performance Computing, Frankfurt, Germany.","DOI":"10.1007\/978-3-319-46079-6_14"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"LeGrand, S., Scheinberg, A., Tillack, A.F., Thavappiragasam, M., Vermaas, J.V., Agarwal, R., Larkin, J., Poole, D., Santos-Martins, D., and Solis-Vasquez, L. (2020, January 21\u201324). GPU-accelerated drug discovery with docking on the summit supercomputer: Porting, optimization, and application to COVID-19 research. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Virtual Event.","DOI":"10.1145\/3388440.3412472"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1021\/acs.jpcb.0c09051","article-title":"Gpu-accelerated flexible molecular docking","volume":"125","author":"Fan","year":"2021","journal-title":"J. Phys. Chem. B"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3910","DOI":"10.1021\/acs.jctc.0c00145","article-title":"Accelerated CDOCKER with GPUs, parallel simulated annealing, and fast Fourier transforms","volume":"16","author":"Ding","year":"2020","journal-title":"J. Chem. Theory Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1093\/bioinformatics\/btz958","article-title":"METADOCK 2: A high-throughput parallel metaheuristic scheme for molecular docking","volume":"37","author":"Serrano","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_30","unstructured":"Shin, J.H., Kim, J., Chae, J., and Yun, S.J. (2022, March 27). GPU-Accelerated Autodock Vina: Viking. Available online: https:\/\/www.morressier.com\/o\/event\/5e733c5acde2b641284a7e27\/article\/5e73656bcde2b641284aa4e5."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Solis-Vasquez, L., Santos-Martins, D., Tillack, A.F., Koch, A., Eberhardt, J., and Forli, S. (2020, January 11). Parallelizing Irregular Computations for Molecular Docking. Proceedings of the 2020 IEEE\/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3), Atlanta, GA, USA.","DOI":"10.1109\/IA351965.2020.00008"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kannan, S., and Ganji, R. (2010, January 18\u201323). Porting autodock to CUDA. Proceedings of the IEEE Congress on Evolutionary Computation, Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586277"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1021\/jm061277y","article-title":"Diverse, high-quality test set for the validation of protein-ligand docking performance","volume":"50","author":"Hartshorn","year":"2007","journal-title":"J. Med. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1021\/ci500081m","article-title":"Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results","volume":"54","author":"Li","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The protein data bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/TCBB.2012.82","article-title":"QuickVina: Accelerating AutoDock Vina using gradient-based heuristics for global optimization","volume":"9","author":"Handoko","year":"2012","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: A major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"The distribution of the flora in the alpine zone","volume":"11","author":"Jaccard","year":"1912","journal-title":"New Phytol."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/27\/9\/3041\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:08:25Z","timestamp":1760137705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/27\/9\/3041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,9]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["molecules27093041"],"URL":"https:\/\/doi.org\/10.3390\/molecules27093041","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2021-3qvn2-v4","asserted-by":"object"}]},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,9]]}}}