RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

• URL: http://arxiv.org/abs/2105.00795v1
• Date: Mon, 3 May 2021 12:47:57 GMT
• Title: RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
• Authors: Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Sung-Ju Hwang, Eunho Yang, Jinwoo Shin
• Abstract summary: Retrosynthesis is an emerging research area of deep learning. We propose a new approach that reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining.
• Score: 107.64562550844146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO {database} are given as candidates, our RetCL achieves top-1 exact match accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches.

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