Pharmaceutical-inspired insecticide discovery: Artificial intelligence, chemoinformatics, and target-based design for next-generation insect control.

This review examines how pharmaceutical-inspired discovery logic can help revitalize insecticide innovation by integrating target-based design, chemoinformatics, and artificial intelligence into a more structured discovery pipeline. It outlines the innovation deficit in insecticide discovery and explains why pharmaceutical concepts such as validated target selection, hit-to-lead progression, multi-parameter optimization, and Design-Make-Test-Analyze cycles provide a useful conceptual model for insect-control discovery. The review evaluates established and emerging insect molecular targets, including classical neurophysiological targets and underexploited insect-selective pathways, with attention to structural tractability, ortholog-based selectivity, and resistance relevance. It further synthesizes the roles of chemoinformatics and artificial intelligence in molecular representation, virtual screening, activity prediction, structure-based design, active learning, generative design, and safety-aware optimization. Particular attention is given to the opportunities and limitations of these approaches in the context of sparse insect-specific datasets, uneven assay standardization, applicability-domain constraints, and the persistent gap between computational promise and field-usable products. The review also considers repurposing strategies, scaffold innovation, selectivity and pollinator safety, environmental sustainability, resistance-informed design, and the translational barriers that limit movement from in silico leads to deployable insecticides. Overall, the evidence suggests that the strongest future for insecticide discovery lies not in artificial intelligence alone, but in a connected discovery ecosystem that links target biology, structural insight, chemistry, predictive modeling, validation practice, and sustainability-oriented design.