Fragment-synergy genetic algorithm turns P-gp substrates into non-substrates
- P-gp efflux limits bioavailability and CNS exposure, and ML substrate classifiers rarely translate into concrete design moves.
- SF-GA builds fragment synergy analysis into the mutation step of a genetic algorithm, biasing replacements toward substructures that co-occur in non-substrates.
- Against GA and reinforcement-learning baselines at a fixed evaluation budget it was competitive and gave the most synthetically accessible molecules, reporting the removed and inserted fragments for every accepted mutation.
💡 Points
The part I would use is the per-mutation fragment report: it reads like a matched-pair suggestion list, which is how a chemist actually wants a model to talk. Competitive rather than better optimization performance means the value is interpretability and synthesizability, not raw efficiency. Prospective validation on real efflux assays is the missing piece.