SynKB: Semantic Search for Synthetic Procedures

08/15/2022
by   Fan Bai, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/11/2023

MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures

In this paper, we present a novel approach to knowledge extraction and r...
research
05/16/2022

Reasoning about Procedures with Natural Language Processing: A Tutorial

This tutorial provides a comprehensive and in-depth view of the research...
research
03/15/2023

Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization

We present Mirror, an open-source platform for data exploration and anal...
research
11/18/2017

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Computational synthesis planning approaches have achieved recent success...
research
08/22/2022

NLDS-QL: From natural language data science questions to queries on graphs: analysing patients conditions treatments

This paper introduces NLDS-QL, a translator of data science questions ex...
research
09/01/2021

Latin writing styles analysis with Machine Learning: New approach to old questions

In the Middle Ages texts were learned by heart and spread using oral mea...

Please sign up or login with your details

Forgot password? Click here to reset