Faculty, Staff and Student Publications
Language
English
Publication Date
3-25-2026
Journal
Translational Psychiatry
DOI
10.1038/s41398-026-03971-1
PMID
41876492
PMCID
PMC13040068
PubMedCentral® Posted Date
3-25-2026
PubMedCentral® Full Text Version
Post-print
Abstract
The early detection of potential side effects (SEs) is a critical yet formidable challenge within the realms of drug development and patient healthcare management. Conventional in-vitro or in-vivo approaches for SE detection are often not feasible to scale during the preclinical phase for numerous drug candidates. Innovations in explainable artificial intelligence offer the prospect of early detection of potential SEs for novel therapeutics prior to their release in the market, as well as the explication of the underlying biological mechanisms. In this context, we present a novel biologically informed graph-based model, called HHAN-DSI, which capitalizes on multimodal interactions among molecular entities. Applied within the domain of the central nervous system (CNS) - the organ system associated with the largest number of SEs - our model demonstrates its capability to reveal previously unrecognized SEs of various psychiatric drugs. Moreover, HHAN-DSI elucidates the associated biological mechanisms, delineating an intricate network of genes, biological functions, drugs, and SEs.
Keywords
Humans, Neural Networks, Computer, Drug-Related Side Effects and Adverse Reactions, Central Nervous System, Psychotropic Drugs, Graph Neural Networks
Published Open-Access
yes
Recommended Citation
Huang, Tongtong; Lin, Ko-Hong; Machado-Vieira, Rodrigo; et al., "Explainable Drug Side Effect Prediction in Central Neural System via Biologically Informed Graph Neural Network" (2026). Faculty, Staff and Student Publications. 4319.
https://digitalcommons.library.tmc.edu/uthmed_docs/4319