arXiv:2606.28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design. In this work, we evaluate machine learning (ML) approaches where variants of active learning are used to classify instances of high binding affinity between 9-mer epitopes and a well-conserved swine leukocyte antigen (SLA) receptor in the context of Porcine Reproductive and Respiratory Syndrome (PRRS). W
The convergence of advanced AI methodologies like transformers and active learning with computationally intense biological simulations allows for breakthroughs in areas previously limited by cost and time.
This development showcases how AI can dramatically accelerate the preclinical phase of vaccine development, making it more data-efficient and potentially enabling faster responses to emerging pathogens.
The traditional bottleneck of high-fidelity molecular docking simulations for vaccine epitope selection can be significantly eased, leading to more rapid and cost-effective identification of viable vaccine candidates.
- · Biopharmaceutical companies
- · Veterinary medicine sector
- · AI/ML researchers in life sciences
- · Livestock farming
- · Traditional high-throughput screening methods
- · R&D pipelines reliant solely on physical experimentation
Faster and cheaper development of vaccines for animal diseases, improving food security and economic stability in agricultural sectors.
The validated methodology could be adapted to human pathogen vaccine development, potentially leading to a paradigm shift in pandemic preparedness.
Increased global investment in AI-driven synthetic biology platforms, accelerating drug discovery and bio-manufacturing across multiple health and industrial domains.
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Read at arXiv cs.LG