SynKB: Semantic Search for Synthetic Procedures

Autor:	Bai, Fan, Ritter, Alan, Madrid, Peter, Freitag, Dayne, Niekrasz, John
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this paper we present SynKB, an open-source, 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. Comment: Accepted to EMNLP 2022 Demo track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.07400 Zobrazit plný text záznamu View this record from Arxiv