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- Title
COMA: efficient structure-constrained molecular generation using contractive and margin losses.
- Authors
Choi, Jonghwan; Seo, Sangmin; Park, Sanghyun
- Abstract
Background: Structure-constrained molecular generation is a promising approach to drug discovery. 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. Methods: 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. Results: 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.
- Subjects
DRUG discovery; REINFORCEMENT learning; MOLECULAR structure; CHEMICAL properties; DRUG resistance; LEAD
- Publication
Journal of Cheminformatics, 2023, Vol 15, Issue 1, p1
- ISSN
1758-2946
- Publication type
Academic Journal
- DOI
10.1186/s13321-023-00679-y