让生成的分子更容易合成，用逆合成思路找可行替代品。
SynTwins: A Retrosynthesis-Guided Framework for Synthesizable Molecular Analog Generation
- 通过逆合成+相似片段搜索+虚拟合成三步法，模仿专家设计。
- 在多个数据集上生成分子的可合成性显著优于现有模型。
- 适合药物和材料研发中需要快速生成可合成分子的场景。
AI生成的分子虽具理想性质，但常难以合成，成为药物与材料发现的瓶颈。本文提出SynTwins框架，通过逆合成、寻找相似构建单元、虚拟合成三步策略，引导生成具有可合成性的分子类似物。该方法采用搜索算法而非随机生成，相比主流机器学习模型，在保持与目标分子高结构相似性的同时，显著提升可合成性。集成到现有分子性质优化流程中，仅小幅降低性质评分即可获得可合成产物。在多种分子数据集上的全面测试表明，SynTwins有效弥合了计算设计与实验合成之间的差距，为各类应用提供可快速实现的分子设计解决方案。
原文摘要 · Abstract (English)
The disconnect between AI-generated molecules with desirable properties and their synthetic feasibility remains a critical bottleneck in computational discovery of drugs and materials. While generative AI has accelerated the proposal of candidate molecules, many of these structures prove challenging or impossible to synthesize using established chemical reactions. Here, we introduce SynTwins, a novel retrosynthesis-guided molecule design framework that finds synthetically accessible molecular analogs by emulating expert chemists' strategies in three steps: retrosynthesis, searching similar building blocks, and virtual synthesis. Using a search algorithm instead of a stochastic data-driven generator, SynTwins outperforms state-of-the-art machine learning models at exploring synthetically accessible analogs while maintaining high structural similarity to original target molecules. Furthermore, when integrated into existing molecular property-optimization frameworks, our hybrid approach produces synthetically feasible analogs with minimal loss in property scores. Our comprehensive benchmarking across diverse molecular datasets demonstrates that SynTwins effectively bridges the gap between computational design and experimental synthesis, providing a practical solution for accelerating the discovery of synthesizable molecules with desired properties for a wide range of applications.
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