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.

本文研究了在化学领域中使用自然语言描述来增强预测模型。使用文献手动提取的大量结构化数据来训练化学信息模型是传统的方法。本文引入了一种新的方法 TextReact，该方法直接利用从文献中检索到的文本增强预测化学。TextReact 检索与给定化学反应相关的文本描述，然后将其与反应的分子表示进行对齐。通过在预测器训练中加入辅助的掩码语言模型目标，增强了这种对齐。我们通过两个化学任务进行了实证验证：反应条件推荐和一步逆合成。通过利用文本检索，TextReact 显著优于仅基于分子数据训练的最先进的化学信息模型。