Energy-based View of Retrosynthesis

• URL: http://arxiv.org/abs/2007.13437v2
• Date: Thu, 9 Dec 2021 03:56:08 GMT
• Title: Energy-based View of Retrosynthesis
• Authors: Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai
• Abstract summary: We propose a framework that unifies sequence- and graph-based methods as energy-based models. We present a novel dual variant within the framework that performs consistent training over Bayesian forward- and backward-prediction. This model improves state-of-the-art performance by 9.6% for template-free approaches where the reaction type is unknown.
• Score: 70.66156081030766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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