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@article{204824,
        author = {Pratik Sambhaji Sawai and Deekshitha P and Vaishnavi pawar and Bhoomika Varshney and Hridya Ramesh and Adinath Palve and Vipin Hiremath},
        title = {AI-Based Computational Vaccine Design against SARS-CoV-2},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4310-4322},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204824},
        abstract = {The cases of highly pathogenic viruses such as zika virus, Ebola virus, Marburg virus, and SARS-CoV-2 are increasing constantly and posing serious threats to global public health and highlighting the critical need for quick, safe, and efficient preventive measures against these diseases [1]. Vaccination remains one of the most effective strategies for preventing viral infections. However, conventional vaccine development techniques are costly, time-consuming, and limited by stringent biosafety regulations [2]. Due to the integration of artificial intelligence with immune-informatics, it becomes a promising and effective method for the vaccine design. 
Therefore, we are focusing the multi-epitope vaccine design against SARS-CoV-2. For multi-epitope vaccine design, the membrane protein of SARS-CoV-2 was selected as the target due to its high antigenic potential and non-allergenic nature [3] Physicochemical properties of the protein were analyzed using ExPASy ProtParam, while secondary and tertiary structures were predicted using PSIPRED and trRosetta, respectively. Cytotoxic T-lymphocyte (MHC class I) and helper T-lymphocyte (MHC class II) epitopes were predicted using the Immune Epitope Database (IEDB). The selected epitopes were assembled into a multi-epitope vaccine construct using suitable linkers and an adjuvant to enhance immunogenicity [4].
Subsequently, the designed vaccine construct underwent structural assessment, molecular docking analysis using ClusPro, and immune simulation using C-ImmSim. The results demonstrated the potential of the vaccine construct to elicit a strong immune response. Overall, this study provides a computationally validated framework for the rational design of multi-epitope vaccines against high-risk viral pathogens and establishes a foundation for future experimental validation.},
        keywords = {Vaccine, Multi-Epitopes, Artificial Intelligence, Immuno-informatics, Antigenicity, Allergenicity},
        month = {June},
        }