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The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molecules) but has enhanced chemical properties (for lead optimization). Many structure-constrained molecular generation models with superior performance in improving chemical properties have been proposed; however, they still have difficulty producing many novel molecules that satisfy both the high structural similarities to each source molecule and improved molecular properties.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We propose a structure-constrained molecular generation model that utilizes contractive and margin loss terms to simultaneously achieve property improvement and high structural similarity. The proposed model has two training phases; a generator first learns molecular representation vectors using metric learning with contractive and margin losses and then explores optimized molecular structure for target property improvement via reinforcement learning.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We demonstrate the superiority of our proposed method by comparing it with various state-of-the-art baselines and through ablation studies. Furthermore, we demonstrate the use of our method in drug discovery using an example of sorafenib-like molecular generation in patients with drug resistance.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13321-023-00679-y","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T11:04:53Z","timestamp":1674126293000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["COMA: efficient structure-constrained molecular generation using contractive and margin losses"],"prefix":"10.1186","volume":"15","author":[{"given":"Jonghwan","family":"Choi","sequence":"first","affiliation":[]},{"given":"Sangmin","family":"Seo","sequence":"additional","affiliation":[]},{"given":"Sanghyun","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF (2022) Generative models for molecular discovery: Recent advances and challenges. 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