S.N. Faraji
Shiraz University of Medical Sciences, Shiraz, Iran,
Iran, Islamic Republic of
Keywords: mRNA vaccine, bioinformatics, artificial intelligence, automation, personalized medicine
Summary:
The design and development of mRNA vaccines represent one of the most transformative advancements in modern medicine. However, traditional methods for vaccine design are time-consuming, labor-intensive, and expensive. Our innovative platform leverages artificial intelligence (AI) to automate the bioinformatics-driven vaccine development pipeline, enabling rapid and accurate production of mRNA vaccines tailored to specific diseases such as cancer and infectious pathogens. By seamlessly integrating AI with computational biology, this platform addresses critical challenges in vaccine development, offering unprecedented speed, scalability, and precision. The bioinformatics-driven vaccine design process involves several crucial steps, all of which can be automated and optimized using AI. These steps include 5 major modules including 1. Antigen Identification: This initial step involves identifying pathogen-specific or tumor-associated proteins that can serve as vaccine targets. AI-powered algorithms can rapidly analyze pathogen genomes or cancer proteomes to pinpoint highly conserved and immunogenic proteins. 2. Epitope Prediction: Selected antigens are further analyzed to identify immunogenic epitopes (regions recognized by the immune system). AI models trained on immunogenicity data predict the most effective B-cell, T-cell, and MHC-binding epitopes, drastically reducing the time and errors associated with manual selection. 3. Vaccine Construct Design: Using the selected epitopes, the platform designs a construct optimized for mRNA synthesis. AI ensures optimal codon usage, secondary structure stability, and immunogenicity, all while minimizing off-target effects. 4. In Silico Evaluation: Advanced AI simulations evaluate the designed vaccine’s efficacy by predicting immune responses, structural stability, and interaction with immune receptors. 5. Optimization & Feedback: AI models iteratively refine vaccine designs by using outputs from previous steps as inputs for the next stages. For instance, feedback from immune simulations can guide epitope re-selection or construct optimization, creating a closed-loop system for continuous improvement. By automating these steps, the platform eliminates bottlenecks in vaccine R&D and reduces the need for extensive manual intervention. The integration of AI not only accelerates timelines but also ensures data-driven decisions at every stage. This scalable solution has the potential to revolutionize vaccine development for a wide range of diseases, from rare cancers to global pandemics.