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

Bruno N Falcone¹, Michael J Hutcheon¹, Jaffer M Zaidi²

¹School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.

Journal of Chemical Information and Modeling

|June 16, 2026

Summary

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This summary is machine-generated.

We developed a rule-based method for generating novel molecules with user control, improving drug discovery. This approach ensures generated molecules are statistically similar to the source database.

Area of Science:

Computational chemistry
Medicinal chemistry
Drug discovery

Background:

Drug discovery programs require controlled generation of novel molecules.
Existing machine learning methods offer limited user control over molecular generation.
3,5-dimethyl-4-phenylisoxazole derivatives are of interest for bromodomain-containing protein 4 inhibition.

Purpose of the Study:

To present a rule-based approach for de novo molecule generation.
To provide users with greater control over the generated molecules compared to machine learning methods.
To apply the method for generating derivatives of a 3,5-dimethyl-4-phenylisoxazole scaffold.

Main Methods:

Deriving combination rules between molecular fragments from a user-provided database.

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Utilizing a build probability score for each generated molecule.

Applying the method to generate derivatives of a 3,5-dimethyl-4-phenylisoxazole parent structure.

Main Results:

Generated molecules are statistically similar to the source database.
The method allows for constrained optimization at defined vectors.
The approach provides enhanced user control in molecule generation.

Conclusions:

The rule-based approach offers a controllable alternative to machine learning for de novo molecule generation.
This method facilitates the development of targeted drug candidates, such as bromodomain-containing protein 4 inhibitors.
The build probability score aids in assessing the relevance of generated molecules.