{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T02:37:00Z","timestamp":1726022220045},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":15,"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":["U22A2068"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n In the field of pharmacochemistry, it is a time-consuming and expensive process for the new drug development. The existing drug design methods face a significant challenge in terms of generation efficiency and quality.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In this paper, we proposed a novel molecular generation strategy and optimization based on A2C reinforcement learning. In molecular generation strategy, we adopted transformer-DNN to retain the scaffolds advantages, while accounting for the generated molecules\u2019 similarity and internal diversity by dynamic parameter adjustment, further improving the overall quality of molecule generation. In molecular optimization, we introduced heterogeneous parallel supercomputing for large-scale molecular docking based on message passing interface communication technology to rapidly obtain bioactive information, thereby enhancing the efficiency of drug design. Experiments show that our model can generate high-quality molecules with multi-objective properties at a high generation efficiency, with effectiveness and novelty close to 100%. Moreover, we used our method to assist shandong university school of pharmacy to find several candidate drugs molecules of anti-PEDV.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n The datasets involved in this method and the source code are freely available to academic users at https:\/\/github.com\/wq-sunshine\/MomdTDSRL.git.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad693","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T23:01:21Z","timestamp":1700089281000},"source":"Crossref","is-referenced-by-count":5,"title":["Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"http:\/\/orcid.org\/0009-0001-1310-4656","authenticated-orcid":false,"given":"Qian","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , Qingdao, Shandong 266100, China"}]},{"given":"Zhiqiang","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , Qingdao, Shandong 266100, China"}]},{"given":"Xiaotong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , Qingdao, Shandong 266100, China"}]},{"given":"Zhuoya","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for High Performance Computing and System Simulation, National Laboratory for Marine Science and Technology , Qingdao, Shandong 266237, China"}]},{"given":"Yujie","family":"Dong","sequence":"additional","affiliation":[{"name":"Marine Big Data Center of Institute for Advanced Ocean Study, Ocean University of China , Qingdao, Shandong 266100, China"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 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