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	Abstract:Lead optimization in drug discovery requires improving therapeutic properties whereas guaranteeing that molecular modifications correspond to possible artificial routes. Existing approaches either prioritize property scores with out implementing synthesizability, or rely on costly enumeration over giant reaction networks, whereas direct software of Large Language Models (LLMs) to molecular era regularly produces chemically invalid buildings. We introduce MolReAct, a framework that formulates lead optimization as a Markov Decision Process over a synthesis-constrained motion house defined by validated response templates. A software-augmented LLM agent serves as a dynamic reaction environment, invoking specialized chemical analysis tools to determine reactive sites and purposeful groups and proposing a compact set of chemically grounded transformations from matched templates. A dedicated coverage model educated via Group Relative Policy Optimization (GRPO) selects amongst these constrained actions to maximise long-time period oracle reward across multi-step trajectories, with a SMILES-based caching mechanism decreasing finish-to-finish optimization time by roughly 43%. Across 13 property optimization duties from the Therapeutic Data Commons and one construction-primarily based docking job, MolReAct achieves an average Top-10 score of 0.571, the best amongst all baselines, ranking first or second on 13 of 14 duties and attaining one of the best sample efficiency on 9 of 14 tasks. By grounding each optimization step in validated reaction templates, MolReAct produces molecules that aren't only property-improved however each accompanied by an explicit template-grounded artificial pathway.

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