Deep learning‐based prediction of insect odorant receptor–ligand interactions for behavioral regulator discovery
Sibo Wang, Penghao Wang, Aniruddha Agnihotri, Wei Xu, Guirong WangAbstract
BACKGROUND
Widespread chemical insecticide use causes resistance and ecological damage, driving need for sustainable pest control via olfaction‐based behavioral regulators targeting odorant receptors (ORs). Conventional screening is costly and low‐throughput. Deep learning (DL) offers solutions but faces challenges of scarce data, distinct OR–ligand binding properties, and requirements for explainability to guide regulator design.
RESULTS
To explore effective representations of volatile compounds under a limited data setting, we developed three deep learning models based on molecular descriptors (Concat‐Desc), molecular graphs (Concat‐GNN), and Simplified Molecular Input Line Entry System (SMILES) sequences (Cross‐Attn) for OR–ligand interaction prediction. Trained on 4566 experimental pairs from M2iOR database, all DL models outperformed quantitative structure–activity relationship (QSAR) baselines. Concat‐Desc and Cross‐Attn achieved superior performance (test area under the receiver operating characteristic curve (AUC) = 0.891–0.927; accuracy = 0.810–0.843). The explainability modules highlighted model‐associated ligand features and protein sequence positions.
CONCLUSION
Molecular descriptors and SMILES are effective for volatile compounds in data‐limited scenarios, while graph representations require further optimization. Integrated explainability modules provide testable hypotheses for experimental validation and future design, establishing a computational framework for artificial intelligence (AI)‐driven insect chemical ecology. Limitations include data scarcity and limited species coverage that future studies must address. © 2026 Society of Chemical Industry.