Systematic analysis of hepatotoxicity: combining literature mining and AI language models.

ABSTRACT:

Background

The body of toxicological knowledge and literature is expanding at an accelerating pace. This rapid growth presents significant challenges for researchers, who must stay abreast with latest studies while also synthesizing the vast amount of published information.

Goal

Our goal is to automatically identify potential hepatoxicants from over 50,000 compounds using the wealth of scientific publications and knowledge.

Methods

We employ and compare three distinct methods for automatic information extraction from unstructured text: (1) text mining (2) word embeddings and (3) large language models. These approaches are combined to calculate a hepatotoxicity score for over 50,000 compounds. We assess the performance of the different methods with a use case on Drug-I

SUBMITTER: Bauer C 

PROVIDER: S-EPMC12338115 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature