# P-gp Substrate to Nonsubstrate Conversion

P-glycoprotein efflux limits small-molecule bioavailability and central-nervous-system exposure. SF-GA embeds fragment synergy analysis in the mutation step of a genetic algorithm, preferentially swapping in substructures that co-occur with the non-substrate class, so predicted P-gp substrates can be rewritten into predicted non-substrates while the scaffold is retained. On the PMO benchmark under a fixed function-evaluation budget, SF-GA's optimization performance is comparable to GB-GA, REINVENT and FragGT, and it reaches the highest mean RAscore (0.932) among all methods tested. The molecules remain hypothetical computational predictions, and non-substrate behavior has not been experimentally validated.

P-glycoprotein efflux limits small-molecule bioavailability and central-nervous-system exposure. SF-GA embeds fragment synergy analysis in the mutation step of a genetic algorithm, preferentially swapping in substructures that co-occur with the non-substrate class, so predicted P-gp substrates can be rewritten into predicted non-substrates while the scaffold is retained. On the PMO benchmark under a fixed function-evaluation budget, SF-GA's optimization performance is comparable to GB-GA, REINVENT and FragGT, and it reaches the highest mean RAscore (0.932) among all methods tested. The molecules remain hypothetical computational predictions, and non-substrate behavior has not been experimentally validated.