Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data

Drug discovery inherently involves multiparameter optimization in the molecular design because drug candidate molecules must meet diverse properties such as bioactivity, synthesizability, and pharmacokinetic properties. This optimization has traditionally relied on iterative manual design and experimental testing, which are labor-intensive and time-consuming. There is therefore a strong incentive to develop computational methods that efficiently design drug-like molecules with multiple favorable properties using chemical and biological data on therapeutic targets. This study proposes a novel computational method for multiproperty optimization in the molecular structure design of bioactive molecules using multicontext (i.e., chemically and genetically perturbed) transcriptome data on human cells. We integrate a molecular generative model conditioned on a transcriptome profile observed with the target gene knockdown or overexpression into a reinforcement learning framework, enabling simultaneous optimization of a quantitative estimate of drug-likeness, synthetic accessibility score, and water/octanol partition coefficient, while accounting for system-level biological effects on a therapeutic target. Using comprehensive benchmarking against established baselines and rigorous validation across multiple metrics, we demonstrate that the proposed method consistently yields molecules with more favorable drug-like characteristics than existing methods. This proposed method can help achieve more efficient identification of novel drug candidates.