Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning

MOTIVATION: The generation of high-quality candidate molecules remains a central challenge in AI-driven drug design. Current phenotype-based and target-based strategies each suffer limitations, either incurring high experimental costs or overlooking system-level cellular responses. To bridge this gap, we propose XMolRL, a novel generative framework that synergistically integrates phenotypic and target-specific cues for de novo molecular generation. RESULTS: The phenotype-guided generator is first pretrained on expansive drug-induced transcriptional profiles and subsequently fine-tuned via multi-objective reinforcement learning (RL). Crucially, the reward function fuses docking affinity and drug-likeness scores, augmented with ranking loss, prior-likelihood regularization, and entropy maximization. The multi-objective RL steers the model toward chemotypes that are simultaneously potent, diverse, and aligned with the specified phenotypic effects. Extensive experiments demonstrate XMolRL's superior performance over state-of-the-art phenotype-based and target-based models across multiple well-characterized targets. Our generated molecules exhibit favorable drug-like properties, high target affinity, and inhibitory potency (IC50) against cancer cells. This unified framework showcases the synergistic potential of combining phenotype-guided and target-aware strategies, offering a more effective solution for de novo drug discovery. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at: https://github.com/hliulab/XMolRL. The archived version of the source code and test data can be found at: https://doi.org/10.5281/zenodo.19607680.