Zhao Dengwei, Tu Shikui, Xu Lei
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Guangdong Institute of Intelligence Science and Technology, Zhuhai, China.
Commun Chem. 2024 Mar 7;7(1):52. doi: 10.1038/s42004-024-01133-2.
Retrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in synthetic chemistry. Computer-aided retrosynthesis has made significant progress, in which heuristic search algorithms, including Monte Carlo Tree Search (MCTS) and A search, have played a crucial role. However, unreliable guiding heuristics often cause search failure due to insufficient exploration. Conversely, excessive exploration also prevents the search from reaching the optimal solution. In this paper, MCTS exploration enhanced A (MEEA) search is proposed to incorporate the exploratory behavior of MCTS into A by providing a look-ahead search. Path consistency is adopted as a regularization to improve the generalization performance of heuristics. Extensive experimental results on 10 molecule datasets demonstrate the effectiveness of MEEA. Especially, on the widely used United States Patent and Trademark Office (USPTO) benchmark, MEEA achieves a 100.0% success rate. Moreover, for natural products, MEEA successfully identifies bio-retrosynthetic pathways for 97.68% test compounds.
逆合成规划旨在确定从起始原料到目标分子的合成途径，是合成化学中的一个基本问题。计算机辅助逆合成已取得显著进展，其中启发式搜索算法，包括蒙特卡罗树搜索（MCTS）和A搜索，发挥了关键作用。然而，不可靠的引导启发式方法往往由于探索不足而导致搜索失败。相反，过度探索也会阻碍搜索找到最优解。本文提出了MCTS探索增强A（MEEA）搜索，通过提供前瞻搜索将MCTS的探索行为融入A*搜索。采用路径一致性作为正则化方法来提高启发式方法的泛化性能。在10个分子数据集上的大量实验结果证明了MEEA的有效性。特别是，在广泛使用的美国专利商标局（USPTO）基准测试中，MEEA实现了100.0%的成功率。此外，对于天然产物，MEEA成功地为97.68%的测试化合物确定了生物逆合成途径。
