用生成模型设计可合成的分子，提升药物研发效率
Generative Artificial Intelligence for Navigating Synthesizable Chemical Space
- 基于Transformer与扩散模块生成可合成的分子路径
- 在局部与全局化学空间探索中均优于现有方法
- 适合药物发现与材料科学领域的研究人员使用
我们提出SynFormer，一种用于高效探索可合成化学空间的生成建模框架。不同于传统分子生成方法，SynFormer通过生成分子的合成路径，确保设计结果具备可合成性。该框架结合可扩展的Transformer架构和用于构建模块选择的扩散模块，在可合成分子设计上超越现有模型。我们展示了SynFormer在两个关键应用中的有效性：(1) 局部化学空间探索，即生成参考分子的可合成类似物；(2) 全局化学空间探索，即根据黑盒性质预测代理识别最优分子。此外，我们通过计算资源增加时性能提升，验证了方法的可扩展性。代码与训练模型已开源，期望在药物发现与材料科学中广泛应用。
原文摘要 · Abstract (English)
We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approaches, we generate synthetic pathways for molecules to ensure that designs are synthetically tractable. By incorporating a scalable transformer architecture and a diffusion module for building block selection, SynFormer surpasses existing models in synthesizable molecular design. We demonstrate SynFormer's effectiveness in two key applications: (1) local chemical space exploration, where the model generates synthesizable analogs of a reference molecule, and (2) global chemical space exploration, where the model aims to identify optimal molecules according to a black-box property prediction oracle. Additionally, we demonstrate the scalability of our approach via the improvement in performance as more computational resources become available. With our code and trained models openly available, we hope that SynFormer will find use across applications in drug discovery and materials science.
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