Reinforcement Learning for De Novo RET Inhibitor Design: Potency-Focused Optimization and Polypharmacological Multi-Parameter Optimization Against Resistance Mutations

Despite their role as oncogenic drivers and predictive biomarkers, alterations in rearrangement during transfection (RET), a receptor tyrosine kinase (RTK), remain significant challenges due to off-target effects and reduced efficacy against emerging mutations, necessitating more selective and innovative drug design. Herein, we report a multi-layered framework encompassing both reinforcement learning (RL) training and post-processing phases, for the efficient de novo design of RET inhibitors striking a balance between structural novelty and predictive reliability. We evaluated two RL strategies: potency-focused optimization (PFO) and polypharmacological multi-parameter optimization (PMPO), with the latter integrating 131 predictive models for off-target selectivity and phenotypic activity. For the 23 kinase targets, pIC50 classification thresholds were defined using Youden's J statistic (mean J = 0.762, range 0.679–0.918). The 108 NSCLC phenotype models showed limited discriminative performance (mean AUC = 0.550). After applicability domain filtering, the increased mean AUC allowed the use of the phenotype models as a confidence-weighted filter in the generative workflow. Post-hoc similarity distribution analysis confirmed that the generated candidates represent structurally novel yet domain-consistent chemotypes, as evidenced by peak densities situated within the Tanimoto similarity range of 0.20–0.40 (generated vs known). Notably, PMPO significantly enhanced multi-parameter consistency (CV: 5.1