用图结构强化学习生成更优药物分子，兼顾化学与拓扑信息。
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
- 基于多尺度加权彩色图与持久同调构建分子状态空间
- 在结合亲和力预测上优于现有方法，提升药物设计效率
- 适合药物发现、分子生成领域研究者参考
药物分子的生成对药物设计至关重要。现有强化学习方法常忽略分子结构信息，而基于特征工程的方法通常仅关注结合亲和力预测，缺乏实质性分子修改。为此，我们提出图结构拓扑强化学习（GraphTRL），融合化学与结构数据以提升分子生成效果。GraphTRL利用多尺度加权彩色图（MWCG）和持久同调，结合分子指纹作为强化学习的状态空间。评估结果表明，GraphTRL在结合亲和力预测任务中优于现有方法，为加速药物发现提供了有前景的新途径。
原文摘要 · Abstract (English)
The generation of drug-like molecules is crucial for drug design. Existing reinforcement learning (RL) methods often overlook structural information. However, feature engineering-based methods usually merely focus on binding affinity prediction without substantial molecular modification. To address this, we present Graph-based Topological Reinforcement Learning (GraphTRL), which integrates both chemical and structural data for improved molecular generation. GraphTRL leverages multiscale weighted colored graphs (MWCG) and persistent homology, combined with molecular fingerprints, as the state space for RL. Evaluations show that GraphTRL outperforms existing methods in binding affinity prediction, offering a promising approach to accelerate drug discovery.
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