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 onestep retrosynthesis.By leveraging text retrieval, TextReact significantly outperforms state-ofthe-art chemoinformatics models trained solely on molecular data.

Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route. In this work, we propose a novel framework that utilizes context information for improved retrosynthetic planning. We view synthetic routes as reaction graphs and propose to incorporate context through three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. Our approach is the first attempt to utilize in-context learning for retrosynthesis prediction in retrosynthetic planning. The entire framework can be efficiently optimized in an end-to-end fashion and produce more practical and accurate predictions. Comprehensive experiments demonstrate that by fusing in the context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes. Code is available at https://github.com/SongtaoLiu0823/FusionRetro.

Mohan Zhang, Luchen Tan, Zihang Fu, Kun Xiong, Jimmy Lin, Ming Li, Zhengkai Tu. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.