Transfer learning reveals species-specific olfactory preferences in Diptera and informs pest management strategies

Authors

Zhang, Y., Xu, L., Gao, C., Zhang, T., Duan, S., Yin, Y., Yang, X., Sun, Q., Qin, X., Li, G., Xu, C., Jiang, H., LU, H.-M.

Abstract

The interaction between volatile organic compounds (VOCs) and odorant receptors (ORs), termed VOI, constitutes the molecular basis for species to recognize chemical information in the environment. Characterization of subtle differences in VOI relationships among closely related species is crucial for understanding the rapid evolution of ORs and for identifying species-specific chemical cues relevant to pest management. However, progress in this field has been constrained by both the scarcity of experimental data and limitations in computational prediction accuracy. To address this, we developed a virtual screening-enhanced transfer learning strategy that integrates large-scale molecular docking data with sparse functional experimental data. The resulting VOI prediction model was validated through functional experiments on the pest species Bactrocera dorsalis, demonstrating its cross-species predictive capacity. Using this model, we investigated the relationship between ecological niches and olfactory sensitivity in Diptera insects from multiple perspectives, with an emphasis on patterns that may inform pest behavioral research. The model's predictive reliability was further supported by its consistency with known olfactory trends: it recapitulated the preferential responses of fruit flies and mosquitoes to esters and aromatics, respectively, and reproduced the previously reported negative correlation between olfactory and visual capacities in Drosophila. As a proof-of-concept application, we compared hematophagous and non-hematophagous mosquitoes, revealing overlapping chemical spaces but distinct patterns in their specifically recognized compounds. This study provides a reliable VOI prediction framework that reveals species-level olfactory preference patterns in Diptera, offering a computational pipeline to accelerate the discovery of behaviorally active compounds for species-specific pest monitoring and control.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Jul 2026.

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