Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.

ABSTRACT:

Objective

The coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data.

Methods

This research

SUBMITTER: Supianto AA 

PROVIDER: S-EPMC9810500 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature