Hybrid modeling of Parkinsons disease integrating zebrafish neurobiology with in silico predictive analytics

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 review highlights the emerging paradigm of hybrid modeling, combining zebrafish (Danio rerio) experimentation with in silico computational and AI-driven pipelines to advance PD research. Zebrafish provide a powerful 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 integrating these two modalities, hybrid platforms offer a multiscale understanding of PD pathogenesis and allow efficient identification of biomarkers, drug candidates, and gene–environment interactions. This review synthesizes current evidence, methodological advances, challenges, and future directions for establishing zebrafish– in–silico hybrid pipelines as next-generation tools for PD precision research.