pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows

Tong Chen, Maximilian Holsman, Lin Zhao, Yinuo Zhang, Pranam Chatterjee

Keywords: Discrete flow matching, molecular engineering, multi-objective optimization, feasibility constraints, Edit Flows

TL;DR: pCoMole guides discrete edit flows to shrink biomolecules while optimizing multiple property objectives under hard feasibility constraints.

Abstract: Biomolecular therapeutics often start from known sequences and require targeted editing to improve multiple properties while satisfying hard biochemical and manufacturability constraints. Existing generative methods do not jointly support multi-objective optimization, hard feasibility, and sequence editing in discrete, variable-length biological spaces. We introduce **P**areto-**Co**nstrained **Mol**ecule **e**diting (**pCoMole**), a framework built on discrete flow matching that steers a pre-trained Edit Flow toward user-specified preferences while enforcing terminal feasibility. pCoMole defines a feasibility-gated terminal distribution using an augmented Tchebycheff utility and realizes the resulting preference tilt through a Doob-h transform of the underlying edit process. To make this construction practical, we approximate the required harmonic function using short Monte Carlo rollouts over candidate edits, yielding an efficient guided editor with provable preference consistency. We validate pCoMole by shrinking GFP while retaining fluorescence-related properties, shortening diverse Cas9 orthologs while preserving PAM specificity, and compressing peptide binders into short peptidomimetics that optimize seven drug-related properties under hard constraints. Overall, pCoMole enables constraint-aware, Pareto-aligned editing of biomolecular sequences in discrete, variable-length spaces.

Submission Number: 11