ExMolRL: Phenotype-Target Joint Generation of De Novo Molecules via Multi-Objective Reinforcement Learning

• URL: http://arxiv.org/abs/2509.21010v1
• Date: Thu, 25 Sep 2025 11:13:24 GMT
• Title: ExMolRL: Phenotype-Target Joint Generation of De Novo Molecules via Multi-Objective Reinforcement Learning
• Authors: Haotian Guo, Hui Liu,
• Abstract summary: ExMoIRL is a novel generative framework that integrates phenotypic and target-specific cues for de novo molecular generation.<n>It fuses docking affinity and drug-likeness scores, augmented with ranking loss, prior-likelihood regularization, and entropy.<n>Extensive experiments demonstrate ExMoIRL's superior performance over state-of-the-art-based and target-based models.
• Score: 4.998189068886174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 overlook system-level cellular responses. To bridge this gap, we propose ExMoIRL, a novel generative framework that synergistically integrates phenotypic and target-specific cues for de novo molecular generation. 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 ExMoIRL'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.

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