Yin Xiaodan, Wang Xiaorui, Wu Zhenxing, Li Qin, Kang Yu, Deng Yafeng, Luo Pei, Liu Huanxiang, Shi Guqin, Wang Zheng, Yao Xiaojun, Hsieh Chang-Yu, Hou Tingjun
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China.
J Cheminform. 2025 Mar 2;17(1):27. doi: 10.1186/s13321-025-00975-9.
Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt's robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt's universal workflow makes it suitable for various types of molecular optimization tasks.
分子优化是药物研发中的关键步骤，涉及结构修饰以改善候选药物的理想特性。尽管已经提出了许多基于深度学习的分子优化算法，并且它们在基准测试中可能表现良好，但它们通常没有充分关注分子的可合成性，导致优化后的化合物难以合成。为了解决这个问题，我们首先开发了一个通用流程，能够构建针对任何可建立预测模型的特性的功能反应模板库。基于这些功能模板，我们引入了Syn-MolOpt，一种面向合成规划的分子优化方法。在优化过程中，功能反应模板通过有效转化相关结构片段，引导过程朝着特定特性发展。在四个不同的任务中，包括两个与毒性相关的（GSK3β-诱变性和GSK3β-hERG）以及两个与代谢相关的（GSK3β-CYP3A4和GSK3β-CYP2C19）多特性分子优化任务中，Syn-MolOpt优于三个基准模型（Modof、HierG2G和SynNet），突出了其有效性和适应性。此外，对通过Syn-MolOpt优化的分子的合成路线进行可视化，证实了功能反应模板在分子优化中的有效性。值得注意的是，Syn-MolOpt在评分准确性有限的情况下的稳健性能证明了其在实际分子优化应用中的潜力。通过同时考虑优化和可合成性，Syn-MolOpt有望成为分子优化中的一个有价值的工具。科学贡献：Syn-MolOpt兼顾了分子优化和合成，允许针对要优化的特性设计特定于该特性的功能反应模板库，并在优化目标特性的同时为优化后的化合物提供参考合成路线。Syn-MolOpt的通用工作流程使其适用于各种类型的分子优化任务。
