Predictive Chemistry Augmented with Text Retrieval
Yujie Qian Zhening Li Zhengkai Tu Connor W. Coley Regina Barzilay
This paper focuses on using natural language descriptions to enhance
predictive models in the chemistry field. Conventionally, chemoinformatics
models are trained with extensive structured data manually extracted from the
literature. In this paper, we introduce TextReact, a novel method that directly
augments predictive chemistry with texts retrieved from the literature.
TextReact retrieves text descriptions relevant for a given chemical reaction,
and then aligns them with the molecular representation of the reaction. This
alignment is enhanced via an auxiliary masked LM objective incorporated in the
predictor training. We empirically validate the framework on two chemistry
tasks: reaction condition recommendation and one-step retrosynthesis. By
leveraging text retrieval, TextReact significantly outperforms state-of-the-art
chemoinformatics models trained solely on molecular data.