PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds

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.