From in silico screening to mechanism: computational advances in developing anti-allergic agents for food allergy

Food allergy represents a significant global health concern with considerable socioeconomic impacts. While natural bioactive components offer promising intervention potential, their discovery and mechanistic elucidation through traditional experimental methods remain constrained by high costs, low throughput, and extended research cycles. Computational approaches have emerged as powerful and efficient alternatives in this field, providing increasingly interpretable tools for accelerating research on anti-allergic intervention strategies. This review examines major computational methodologies for food allergy intervention research, including quantitative structure-activity relationship (QSAR) modeling, molecular docking, molecular dynamics (MD) simulations, quantum chemical calculations, network pharmacology, multi-omics integration, and AI technologies. We outline their principles, workflows, applications, advantages, and limitations, with case studies demonstrating their practical utility in both discovery and mechanistic investigation. These methods enable efficient virtual screening, design, and prioritization of anti-allergic candidates from large libraries, providing multi-scale insights from atomic-level interactions to system-level regulatory networks. Beyond accelerating discovery, these approaches support structural optimization, synergy analysis, and early safety evaluation. Future development should emphasize deeper multi-scale integration, enhanced interpretability, improved adaptability to real food systems and processing conditions, and stronger synergy between computation and experiments, thereby promoting the rational design of anti-allergic functional foods, precision nutrition strategies, and industrial translation.