De novo drug design is a crucial component of modern drug development, yet navigating the vast chemical space to find synthetically accessible, high-affinity candidates remains a significant challenge. Reinforcement Learning (RL) enhances this process by enabling multi-objective optimization and exploration of novel chemical space - capabilities that traditional supervised learning methods lack. In this work, we introduce \textbf{ReACT-Drug}, a fully integrated, target-agnostic molecular design framework based on Reinforcement Learning. Unlike models requiring target-specific fine-tuning, ReACT-Drug utilizes a generalist approach by leveraging ESM-2 protein embeddings to identify similar proteins for a given target from a knowledge base such as Protein Data Base (PDB). Thereafter, the known drug ligands corresponding to such proteins are decomposed to initialize a fragment-based search space, biasing the agent towards biologically relevant subspaces. For each such fragment, the pipeline employs a Proximal Policy Optimization (PPO) agent guiding a ChemBERTa-encoded molecule through a dynamic action space of chemically valid, reaction-template-based transformations. This results in the generation of \textit{de novo} drug candidates with competitive binding affinities and high synthetic accessibility, while ensuring 100\% chemical validity and novelty as per MOSES benchmarking. This architecture highlights the potential of integrating structural biology, deep representation learning, and chemical synthesis rules to automate and accelerate rational drug design. The dataset and code are available at https://github.com/YadunandanRaman/ReACT-Drug/.

翻译：从头药物设计是现代药物研发的关键环节，然而在广阔的化学空间中寻找可合成且具有高亲和力的候选分子仍是一项重大挑战。强化学习通过支持多目标优化和探索新颖化学空间的能力，增强了这一过程——这是传统监督学习方法所欠缺的。本文中，我们介绍了 **ReACT-Drug**，一个基于强化学习的、完全集成的、与靶点无关的分子设计框架。与需要针对特定靶点进行微调的模型不同，ReACT-Drug 采用通用策略，利用 ESM-2 蛋白质嵌入从知识库（如蛋白质数据库 PDB）中为给定靶点识别相似蛋白质。随后，将这些已知蛋白质对应的药物配体分解，以初始化一个基于片段的搜索空间，从而引导智能体偏向于生物学相关的子空间。对于每个这样的片段，该流程采用一个近端策略优化智能体，引导一个由 ChemBERTa 编码的分子，在一个基于化学反应模板的、化学上有效的动态动作空间中进行转换。这最终生成了具有竞争性结合亲和力与高合成可及性的 *从头* 药物候选分子，同时根据 MOSES 基准测试确保了 100% 的化学有效性和新颖性。该架构凸显了整合结构生物学、深度表征学习与化学合成规则，以实现理性药物设计自动化与加速的潜力。数据集和代码可在 https://github.com/YadunandanRaman/ReACT-Drug/ 获取。