Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention,JACS Au

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Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
JACS Au ( IF 8.5 ) Pub Date : 2021-08-05 , DOI: 10.1021/jacsau.1c00246
Shuan Chen ₁ , Yousung Jung ₁

Affiliation

As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. Here we propose a local retrosynthesis framework called LocalRetro, motivated by the chemical intuition that the molecular changes occur mostly locally during the chemical reactions. This differs from nearly all existing retrosynthesis methods that suggest reactants based on the global structures of the molecules, often containing fine details not directly relevant to the reactions. This local concept yields local reaction templates involving the atom and bond edits. Because the remote functional groups can also affect the overall reaction path as a secondary aspect, the proposed locally encoded retrosynthesis model is then further refined to account for the nonlocal effects of chemical reaction through a global attention mechanism. Our model shows a promising 89.5 and 99.2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions. We further demonstrate the validity of LocalRetro on a large dataset containing 479 035 reactions (UTPTO-MIT) with comparable round-trip top-1 and top-5 accuracy of 87.0 and 97.4%, respectively. The practical application of the model is also demonstrated by correctly predicting the synthesis pathways of five drug candidate molecules from various literature.

中文翻译：

使用局部反应性和全局注意力的深度逆合成反应预测

作为化学中的一个基本问题，逆合成旨在设计目标化合物的反应途径和中间体。人工智能 (AI) 辅助逆合成的目标是通过从以前的化学反应中学习以做出新的预测，从而使这一过程自动化。尽管一些模型已经证明了它们在自动逆合成方面的潜力，但仍然需要将预测精度进一步提高到更实用的水平。在这里，我们提出了一个名为LocalRetro的局部逆合成框架，受化学直觉的启发，即在化学反应过程中分子变化主要发生在局部。这不同于几乎所有现有的逆合成方法，这些方法基于分子的整体结构建议反应物，通常包含与反应不直接相关的精细细节。这个局部概念产生了涉及原子和键编辑的局部反应模板。由于远程官能团也可以作为次要方面影响整体反应路径，因此进一步完善了所提出的局部编码逆合成模型，以通过全局注意机制解释化学反应的非局部效应。我们的模型在包含 50 016 个反应的 USPTO-50K 数据集的 top-1 和 top-5 预测中显示出有希望的 89.5% 和 99.2% 的往返准确度。LocalRetro在包含 479 035 个反应 (UTPTO-MIT) 的大型数据集上，具有可比的往返 top-1 和 top-5 准确率，分别为 87.0% 和 97.4%。通过正确预测来自各种文献的五种候选药物分子的合成途径，也证明了该模型的实际应用。

更新日期：2021-08-05

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