Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Philippe Schwaller [ Teodoro Laino [ Théophile Gaudin [ Peter Bolgar [ Costas Bekas [ Alpha A Lee [
Abstract
Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.
中文速览
预测有机化学反应的产物（正向合成预测，forward reaction prediction）是药物研发中长期未解决的难题，现有方法要么依赖人工规则和原子映射模板，要么在准确率和不确定性估计上存在明显短板。研究团队将反应预测转化为一个"化学语言翻译"任务，把反应物和试剂的SMILES字符串"翻译"成产物的SMILES字符串，并采用完全基于多头注意力机制的Transformer模型（称为Molecular Transformer）来完成这一翻译，无需任何手工规则或原子映射信息。在常用基准数据集上，该模型的top-1准确率超过90%，全面超越此前所有方法，还能正确处理化学选择性、区域选择性乃至立体化学等精细差异；更重要的是，模型能可靠地估计自身预测的不确定性（ROC-AUC达0.89），让化学家在规划多步合成路线时可以量化每一步的"风险"，优先尝试把握更大的步骤。该工作已部署在IBM RXN化学平台上，被数千名有机化学家实际使用，为数据驱动的自动化合成规划提供了切实可用的工具。
原文 arXiv:1811.02633;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1811.02633v2