ArticleJournal of chemical information and modeling2026
PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
We present a rule-based approach for the generation of new molecules, which derives combination rules between molecular fragments from a user-provided database, and is useful for constrained optimization at defined vectors. The molecules generated are statistically similar to the database provided. Compared to existing machine-learning approaches this affords the user a greater level of control of the molecules generated, which is a common desideratum in drug discovery programs. This is complemented by the use of a build probability score for each generated molecule, which corresponds to the likelihood of its existence in a hypothetical exhaustive enumeration of the source database fragments. The method is applied to the generation of derivatives of a 3,5-dimethyl-4-phenylisoxazole parent structure, a core of current interest for the development of inhibitors of bromodomain-containing protein 4.
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