Synergy-Guided Molecular Redesign of Predicted P-Glycoprotein Substrates toward Synthetically Feasible Non-Substrates

This is the authors' abstract. We don't add key points for this paper.

Drug efflux mediated by P-glycoprotein (P-gp) remains a major barrier to effective small-molecule therapeutics, limiting both bioavailability and central nervous system exposure. While machine learning models can predict P-gp substrates, translating these predictions into actionable molecular design remains largely unexplored, particularly in enabling the rational conversion of substrates into nonsubstrates to mitigate efflux liability. Here, we introduce the synergy-guided fragment genetic algorithm (SF-GA), a computational framework that transforms predictive insight into an interpretable molecular design strategy. By integrating fragment synergy analysis into the mutation stage of a genetic algorithm, SF-GA biases fragment replacement toward co-occurring substructures associated with nonsubstrate classification, enabling interpretability at the chemical structure level and property-directed molecular optimization. Compared with genetic-algorithm and reinforcement-learning baselines under a fixed function-evaluation budget, SF-GA achieves competitive optimization performance while producing molecules with the highest retrosynthetic accessibility among all methods tested. Unlike the baselines, every accepted mutation is reported together with the discarded and inserted fragments and their synergy scores, providing a fragment-level rationale for each structural change; ablation against an unguided and an importance-only control isolates the contribution of bit co-occurrence. The method enables scaffold-preserving and similarity-constrained editing that converts P-gp substrates into predicted nonsubstrates. Docking against two human P-gp structures reveals weaker predicted binding for molecules with higher predicted nonsubstrate probability, motivating future experimental evaluation of P-gp transport.