BIOCHEMICAL TEXT MINING FOR ADVANCING CHEMICAL AND PHARMACEUTICAL KNOWLEDGE

Grant number: LP160101469 | Funding period: 2018 - 2021

Active

Abstract

The project aims to develop novel natural language processing methods to find, extract and structure complex chemical reaction information in scientific literature. The project addresses a recognised bottleneck to efficiency in the drug discovery process, by enabling biochemical research results to be turned into actionable information. This has the potential to inform and accelerate development of effective drug treatments through the linking of relevant biochemical information. By delivering new methods that improve the compilation of knowledge about chemicals and drugs from textual information resources, the project hopes to enable faster drug discovery.

Related publications (3)

Scholarly works icon

ChEMU: Named entity recognition and event extraction of chemical reactions from patents

DQ Nguyen, Z Zhai, H Yoshikawa, B Fang, C Druckenbrodt, C Thorne, R Hoessel, SA Akhondi, T Cohn, T Baldwin, K Verspoor

2020-01-01

We introduce a new evaluation lab named ChEMU (Cheminformatics Elsevier Melbourne University), part of the 11th Conference and Lab..

Scholarly works icon

From POS tagging to dependency parsing for biomedical event extraction

Quoc Nguyen Dat, Karin Verspoor

2019-02-12

Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthe..

Scholarly works icon

End-to-end neural relation extraction using deep biaffine attention

DQ Nguyen, K Verspoor

2019-01-01

© Springer Nature Switzerland AG 2019. We propose a neural network model for joint extraction of named entities and relations betw..

Researchers