用量子启发强化学习设计可合成药物分子，提升优化效率。
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
- 结合强化学习与遗传算法，智能搜索化学结构空间。
- 在10,000次查询预算下，性能优于当前顶尖遗传算法方法。
- 适合药物发现中需高效生成可合成分子的研究者。
可合成分子设计是药物发现中的核心问题，旨在根据药物相关目标函数设计新型分子结构，同时保证合成可行性。现有方法多依赖随机搜索，效率低下。本文提出一种基于量子启发模拟退火策略神经网络的强化学习方法，通过确定性REINFORCE算法输出状态转移概率，指导化学结构空间的智能探索，并在每轮迭代中利用遗传算法进行局部优化以逼近局部最优解。在10,000次查询预算的Practical Molecular Optimization（PMO）基准框架上评估，该方法展现出与当前最先进的遗传算法方法相当甚至更优的性能。
原文摘要 · Abstract (English)
Synthesizable molecular design (also known as synthesizable molecular optimization) is a fundamental problem in drug discovery, and involves designing novel molecular structures to improve their properties according to drug-relevant oracle functions (i.e., objective) while ensuring synthetic feasibility. However, existing methods are mostly based on random search. To address this issue, in this paper, we introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search using genetic algorithm to refine solutions to a local optimum within each iteration. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget. We further showcase the competitive performance of our method by comparing it against the state-of-the-art genetic algorithms-based method.
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