Academic Journal
Pharmaceutical-inspired insecticide discovery: Artificial intelligence, chemoinformatics, and target-based design for next-generation insect control.
| Τίτλος: | Pharmaceutical-inspired insecticide discovery: Artificial intelligence, chemoinformatics, and target-based design for next-generation insect control. |
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| Συγγραφείς: | Ogungbite OC; Department of Plant Science and Biotechnology, Ekiti State University, Ado-Ekiti, Nigeria. Electronic address: olaniyi2oguns@gmail.com., Ogungbite AB; Department of Computer Science, University of South Africa, Florida Campus, South Africa., Ogunlade B; Molecular Pharmacology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA., Akinluyi ET; Department of Chemistry, University of Alberta, Edmonton, Canada. |
| Πηγή: | Pesticide biochemistry and physiology [Pestic Biochem Physiol] 2026 Aug; Vol. 222, pp. 107190. Date of Electronic Publication: 2026 Jun 04. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Academic Press Country of Publication: United States NLM ID: 1301573 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9939 (Electronic) Linking ISSN: 00483575 NLM ISO Abbreviation: Pestic Biochem Physiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York Ny : Academic Press |
| Ιατρικοί όροι (MeSH): | Insecticides*/pharmacology , Insecticides*/chemistry , Insect Control*/methods , Artificial Intelligence* , Drug Discovery* , Cheminformatics*, Insecta/drug effects ; Animals ; Drug Design |
| Περίληψη: | 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. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Chemoinformatics; Crop protection; Insecticide discovery; Molecular targets; Target-based design |
| Substance Nomenclature: | 0 (Insecticides) |
| Entry Date(s): | Date Created: 20260723 Date Completed: 20260723 Latest Revision: 20260723 |
| Update Code: | 20260724 |
| DOI: | 10.1016/j.pestbp.2026.107190 |
| PMID: | 42493009 |
| Βάση Δεδομένων: | MEDLINE |
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