An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions.

ABSTRACT:

Background

Adverse drug reactions (ADRs) present challenges to patient safety and healthcare systems. Current pharmacovigilance methods, such as the Yellow Card Scheme (YCS), provide valuable post-marketing data, but the mechanistic causes of these ADRs are not fully understood. Leveraging drug-target interaction data with interpretable machine learning offers a promising approach to anticipate ADRs and understand their underlying mechanisms.

Objective

This study proposes an interpretable machine learning (ML) framework to predict significant ADRs using drug-target interaction data. The framework aims to identify key pharmacological relationships, helping to inform drug safety.

Methods

Drug-target interaction data from STITCH was combined with ADR reports from the YCS

SUBMITTER: Roberts-Nuttall J 

PROVIDER: S-EPMC12858017 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature