Predictive Chemistry Augmented with Text Retrieval
Name
TextReact.pdf
Description
Accepted version
Size
1.19 MB
Format
Adobe PDF
Checksum (MD5)
981c5f2e1af4275e9800f32cb692222c
Author(s)
Qian, Yujie
Li, Zhening
Tu, Zhengkai
Coley, Connor
Barzilay, Regina
Date Issued
December 2023
Journal
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Citation
Yujie Qian, Zhening Li, Zhengkai Tu, Connor Coley, and Regina Barzilay. 2023. Predictive Chemistry Augmented with Text Retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12731–12745, Singapore. Association for Computational Linguistics.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
10.18653/v1/2023.emnlp-main.784
