Kmail, Mwafaq and Mohamed, Wael Mohamed Yousef
(2026)
Accelerating parkinson’s disease discovery: an in silico Zebrafish predictive model.
ScienceBank, NA (NA).
p. 1.
ISSN 3066-1536
Abstract
Parkinson’s Disease (PD) remains a major neurodegenerative disorder lacking disease-modifying therapies. Traditional single model approaches often fail to capture the complex molecular, environmental, and genetic interactions that drive disease heterogeneity. This narrative review focuses on the emerging paradigm of hybrid modeling, using zebrafish (Danio rerio) experimentation with in silico computational and AI-driven pipelines to advance PD research. Zebrafish provide a strong in vivo system to study dopaminergic neurodegeneration, mitochondrial dysfunction, oxidative stress, and behavioral phenotypes with high translational value. In parallel, computational neuroscience and systems biology tools, including network pharmacology, molecular docking, virtual screening, transcriptomic profiling, and machine-learning–based predictive models, enable rapid hypothesis generation and therapeutic discovery. By combining these two modalities, hybrid platforms help to understand of PD pathogenesis and allow effective identification of biomarkers, drug candidates, and gene–environment interactions. This review highlights the current evidence, methodological advances, challenges, and future directions for establishing zebrafish–in–silico hybrid pipelines as next-generation tools for PD precision research. Importantly, this review proposes an integrated framework that bridges in vivo zebrafish models with in silico and AI-driven approaches, offering a novel strategy to accelerate translational discovery in PD.
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