{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:30:20Z","timestamp":1766068220808,"version":"3.41.2"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Molecular optimization, aiming to identify molecules with improved properties from a huge chemical search space, is a critical step in drug development. This task is challenging due to the need to optimize multiple properties while adhering to stringent drug-like criteria. Recently, numerous effective artificial intelligence methods have been proposed for molecular optimization. However, most of them neglect the constraints in molecular optimization, thereby limiting the development of high-quality molecules that simultaneously satisfy property objectives and constraint compliance. To address this issue, we proposed a deep multi-objective optimization framework, termed CMOMO, for constrained molecular multi-property optimization. The proposed CMOMO divides the optimization process into two stages, which enables it to use a dynamic constraint handling strategy to balance multi-property optimization and constraint satisfaction. Besides, a latent vector fragmentation based evolutionary reproduction strategy is designed to generate promising molecules effectively. Experimental results on two benchmark tasks show that the proposed CMOMO outperforms five state-of-the-art methods to obtain more successfully optimized molecules with multiple desired properties and satisfying drug-like constraints. Moreover, the superiority of CMOMO is verified on two practical tasks, including a potential protein-ligand optimization task of 4LDE protein, which is the structure of $\\beta $2-adrenoceptor GPCR receptor, and a potential inhibitor optimization task of glycogen synthase kinase-3$\\beta $ target (GSK3$\\beta $). Notably, CMOMO demonstrates a two-fold improvement in success rate for the GSK3$\\beta $ optimization task, successfully identifying molecules with favorable bioactivity, drug-likeness, synthetic accessibility, and adherence to structural constraints.<\/jats:p>","DOI":"10.1093\/bib\/bbaf335","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T03:40:20Z","timestamp":1752118820000},"source":"Crossref","is-referenced-by-count":1,"title":["CMOMO: a deep multi-objective optimization framework for constrained molecular multi-property optimization"],"prefix":"10.1093","volume":"26","author":[{"given":"Xin","family":"Xia","sequence":"first","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Artificial Intelligence, Anhui University, Jiulong Road, Hefei 230601,","place":["China"]}]},{"given":"Yajie","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Computer Science and Technology, Anhui University, Jiulong Road, Hefei 230601,","place":["China"]}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering , Hunan University, Lushan Road, Changsha 410012,","place":["China"]}]},{"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Computer Science and Technology, Anhui University, Jiulong Road, Hefei 230601,","place":["China"]}]},{"given":"Chunhou","family":"Zheng","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Artificial Intelligence, Anhui University, Jiulong Road, Hefei 230601,","place":["China"]}]},{"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , School of Artificial Intelligence, Anhui University, Jiulong Road, Hefei 230601,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"volume-title":"Structural Optimization of Drugs: Design Strategies and Empirical Rules","year":"2017","author":"Sheng","key":"2025070923401513300_ref1"},{"key":"2025070923401513300_ref2","doi-asserted-by":"publisher","first-page":"7079","DOI":"10.1039\/D1SC00231G","article-title":"Beyond generative models: Superfast traversal, optimization, novelty, exploration and discovery (stoned) algorithm for molecules using selfies","volume":"12","author":"Nigam","year":"2021","journal-title":"Chem 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