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.

翻译：我们提出SynFormer，一种旨在高效探索和导航可合成化学空间的生成式建模框架。与传统分子生成方法不同，我们通过生成分子的合成路径来确保设计的合成可行性。通过整合可扩展的Transformer架构和用于构建块选择的扩散模块，SynFormer在可合成分子设计方面超越了现有模型。我们通过两个关键应用证明了SynFormer的有效性：(1) 局部化学空间探索，模型生成参考分子的可合成类似物；(2) 全局化学空间探索，模型旨在根据黑盒性质预测预言机识别最优分子。此外，我们通过展示随着计算资源增加而带来的性能提升，证明了我们方法的可扩展性。我们的代码和训练模型已公开提供，我们希望SynFormer能在药物发现和材料科学领域得到广泛应用。