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

Abstract excerpt

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...

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