Retrosynthesis involves determining a sequence of reactions to synthesize complex molecules from simpler precursors. As this poses a challenge in organic chemistry, machine learning has offered solutions, particularly for predicting possible reaction substrates for a given target molecule. These solutions mainly fall into template-based and template-free categories. The former is efficient but relies on a vast set of predefined reaction patterns, while the latter, though more flexible, can be computationally intensive and less interpretable. To address these issues, we introduce METRO (Molecule-Edit Templates for RetrOsynthesis), a machine-learning model that predicts reactions using minimal templates - simplified reaction patterns capturing only essential molecular changes - reducing computational overhead and achieving state-of-the-art results on standard benchmarks.

翻译：逆合成是指确定从简单前体合成复杂分子所需的一系列反应步骤。由于这一过程在有机化学中极具挑战性，机器学习已为此提供了解决方案，尤其是在预测给定目标分子可能的反应底物方面。这些解决方案主要分为基于模板和无模板两类。前者效率较高但依赖于大量预定义的反应模式，后者虽更具灵活性，但计算成本高且可解释性较差。为解决这些问题，我们提出了METRO（分子编辑模板逆合成模型），这是一种利用最小化模板（仅捕捉关键分子变化的简化反应模式）来预测反应的机器学习模型。该方法在降低计算开销的同时，在标准基准测试中达到了当前最优的性能。