Converging XGboost Machine Learning and Molecular Docking Strategies to Identify Attractants for Ceratitis capitata: Molecular Characterization and Database Curation of Natural Ligands for In Vitro/In Vivo Tests
Abstract
The Mediterranean fruit fly Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) is one of the most critical agricultural pests, causing economic damage globally due to its wide range of fruit hosts. Conventional insecticides have brought environmental, human health, and resistance challenges, driving interest in semiochemicals as sustainable pest management alternatives. Potential molecular attractants can be assessed experimentally through methods such as electroantennography (EAG) or behavioral assays. Odorant Binding Proteins (OBPs) have been recognized as crucial mediators in detecting these chemical signals. Although isolated compounds can provide mechanistic insights, volatile blends more accurately reflect natural conditions and typically elicit stronger behavioral responses. However, designing effective blends is challenging due to their complexity and regulatory limitations. Therefore, curated molecular databases of potential attractants become essential to accelerate the discovery and reduce cost in research programs, both in vitro and in vivo tests. The in silico molecular approaches, including Molecular Docking, Molecular Dynamics (MD) and Quantitative Structure-Activity Relationships (QSAR), offer cost-effective methods to prioritize candidates and/or understand ligand-OBP interactions. In this study, computational methodologies including Machine Learning (ML) based QSAR, molecular docking and MD simulations were integrated to highlight molecular features of…
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