Computational immunology in venom research: a systematic review of epitope prediction and validation approaches.

Venom-based therapies are hindered by traditional discovery methods that are costly and inconsistent. Immunoinformatics offers a faster route to identify immunogenic epitopes, yet its application to venom proteins remains limited. We conducted a systematic review under PRISMA-2020 guidelines to identify studies predicting venom toxin epitopes computationally and validating them experimentally. Risk of bias was evaluated using a custom 20-question checklist. Following our systematic search, 11 articles met inclusion criteria. Multitool prediction strategies consistently outperformed single-tool approaches, particularly when structural and sequence-based models were combined. Experimental validations confirmed immunogenicity through diverse assays, but reporting inconsistencies, limited negative data, and variable study designs impaired direct comparison. Toxin family and structural data availability emerged as key factors influencing prediction success. In silico epitope prediction, combined with experimental validation, holds strong promise for advancing venom research. Our systematic bias assessment underscores the critical need for standardized frameworks to evaluate dataset selection, algorithm parameters, and validation rigor in computational epitope discovery. Moreover, the field must urgently address data scarcity, standardize validation protocols, and expand venom-specific training datasets to fully realize the promise of immunoinformatics-driven discovery.