Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based classification models.

ABSTRACT:

Motivation

Drug target selection determines the success of the drug development pipeline. Therefore, novel drug targets need to be assessed for their therapeutic benefits/risks at the earliest stage possible. Where manual risk/benefit analyses are often user-biased and time-consuming, Large Language Models can offer a systematic and efficient approach to curating and analysing literature. Currently, publicly available Large Language Models are lacking for this task, while public platforms for target assessments are limited to co-occurrences.

Results

BERT-models for multi-level classification of drug target-health effect relationships described in PubMed were developed. Relationships were classified based on (i) causality; (ii) direction of target modulation; (iii) direction

SUBMITTER: Venhorst J 

PROVIDER: S-EPMC11919816 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature