pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows
Read the original on arXiv Machine Learning →pCoMole is a new framework that uses discrete flow matching to guide molecule editing toward user-specified multi-objective preferences while enforcing hard feasibility constraints. It introduces a feasibility-gated terminal distribution with an augmented Tchebycheff utility and implements the preference tilt via a Doob‑h transform, approximated with short Monte Carlo rollouts for efficiency. The method is validated on tasks such as shrinking GFP, shortening Cas9 orthologs, and compressing peptide binders, with wet‑lab tests showing that designed eGFP variants retain fluorescence after extensive edits.
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