ArticleTranslational psychiatry2026
Explainable drug side effect prediction in central neural system via biologically informed graph neural network.
Article in Translational psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- Better understanding the phenotypic effects of drugs through shared targets in genetic disease networks.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.