Work

SynKB: Semantic Search for Synthetic Procedures

Year: 2022

Type: conference-paper

Abstract: In this paper we present SYNKB, 1 an opensource, automatically extracted knowledge base of chemical synthesis protocols.Similar to proprietary chemistry databases such as Reaxsys, SYNKB allows chemists to retrieve structured knowledge about synthetic procedures.By taking advantage of recent advances in natural language processing for procedural texts, SYNKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes.Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SYNKB has higher recall than Reaxsys, while maintaining high precision.We plan to make SYNKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.2

Source: Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP)

DOI: 10.18653/v1/2022.emnlp-demos.31

Authors: Fan Bai, Alan Ritter, Peter Madrid, Dayne Freitag, John Niekrasz

Institutions: SRI International, Georgia Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: U.S. Department of Defense, Defense Advanced Research Projects Agency, Advanced Research Projects Agency

Award: G4465369318