FragGT: Fragment-based evolutionary molecule generation with gene types

19 February 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


De novo molecular design is effective for optimizing compounds with desirable properties in drug discovery. In the realm of chemoinformatics, population-based evolutionary approaches have been known for over a decade including those that use retrosynthetically inspired fragment-based workflows to to balance the generation of synthesizable molecules with a sufficiently expressive molecular generator. We describe FragGT, a fragment-based de novo molecular design approach that uses an evolutionary algorithm to optimize molecules towards an objective function. We rely on the concept of gene types to hop between related molecular fragments where a fragment's gene type is defined by the nature of its connections to adjacent groups. We show that this simple approach achieves competitive performance on the GuacaMol benchmark suite with a low computational cost and the additional benefits of transparency and practicality.


Generative Chemistry


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