论文标题
基于能量的回合合成的视图
Energy-based View of Retrosynthesis
论文作者
论文摘要
反循环 - 鉴定一组反应物合成靶分子的过程 - 对于材料设计和药物发现至关重要。现有的基于语言模型和图形神经网络的机器学习方法取得了令人鼓舞的结果。在本文中，我们提出了一个将基于序列和图的方法统一的框架作为具有不同能量函数的基于能量的模型（EBM）。这种统一的观点通过对绩效的全面评估提供了有关EBM变体的关键见解。此外，我们在框架内提出了一种新颖的双重变体，该变体通过约束两个方向之间的一致性，对贝叶斯前向和向后预测进行一致的训练。对于未知反应类型的无模板方法，该模型将最新性能提高了9.6％。
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine learning approaches based on language models and graph neural networks have achieved encouraging results. In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions. This unified perspective provides critical insights about EBM variants through a comprehensive assessment of performance. Additionally, we present a novel dual variant within the framework that performs consistent training over Bayesian forward- and backward-prediction by constraining the agreement between the two directions. This model improves state-of-the-art performance by 9.6% for template-free approaches where the reaction type is unknown.
