Artificial Intelligence and Predictive Analytics in Biopesticide Development: A Systematic Review

Shaifali Mathur and Rachana Tiwari

Biopesticides are gaining recognition as a sustainable pest management strategy since they reduce environmental toxicity and human health hazards while maintaining crop protection and agricultural output. Still, their discovery and development remain time-consuming and resource-intensive due to the biological complexity of microbial metabolism, plant secondary metabolites, peptide stability, RNA interference mechanisms and formulation behavior. By facilitating data-driven discovery, optimization and risk assessment throughout the biopesticide development process, artificial intelligence and integrated predictive analytics are emerging as potent tools to address these issues. The ability to find and rank bioactive candidates at scale has greatly increased by genome and sequence-informed techniques like biosynthetic gene cluster mining, multi-omics integration, structural prediction and deep learning-based peptide discovery. Predictive modeling for non-target risk assessment has been enhanced simultaneously by developments in computational toxicology and ecotoxicological evaluations, reinforced by carefully selected datasets, quantitative structure–activity relationship models and graph-based learning techniques. Additionally, model generalizability is limited by heterogeneous datasets and environmental variability in downstream stages like formulation design, production optimization and field translation. The present systematic review examines literature published from 2011 to 2026, employing structured search strategies and critically evaluates the impact of artificial intelligence-driven technological advancements in biopesticide development across microbial, botanical, peptide/protein and RNAi-based biopesticide modalities. The study also delineates significant methodological constraints and proposes future research trajectories to expedite the development of safe, effective and environmentally sustainable biopesticides.

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