Instance-Wise Contrastive Graph Neural Network Enables the Discovery of Novel Aedes aegypti Larvicidal Compounds

Aedes aegypti remains a major arboviral vector, making larval control a critical strategy to reduce mosquito populations. However, resistance to commercial larvicides has reduced the long-term effectiveness of current interventions, reinforcing the need for new compounds with improved potency and selectivity. Here, we present an instance-wise contrastive graph neural network (GNN) framework to accelerate the discovery of novel larvicidal compounds. The model was trained on a curated dataset of 556 organic compounds organized into LC50-derived multitask classification thresholds and integrated Transformer-inspired graph learning with whole-molecule and fragment-level contrastive regularization. This model achieved strong held-out performance, with global AUC = 0.95 +/- 0.01, PR-AUC = 0.93 +/- 0.01, and MCC = 0.77 +/- 0.03, outperforming conventional machine learning and graph-based baselines. Predictive uncertainty analysis and counterfactual maps further supported the interpretation of threshold-sensitive predictions and substructural contribution patterns. The model was applied to screen 1.3 million compounds, resulting in 10 candidates for experimental validation. Three compounds showed measurable larvicidal activity against A. aegypti larvae. Among them, LC-79 emerged as the most promising hit, with 2-day and 5-day LC50 values of 0.24 microg/mL (0.66 microM) and 0.05 microg/mL (0.13 microM), respectively, an IE50 of 0.06 microg/mL (0.16 microM), and rapid larval mortality (LT50 = 1.10 days at 1 microg/mL). LC-79 also showed no measurable acute toxicity to Daphnia magna at the highest tested concentration [EC50-48h >43 microg/mL (>119 microM)], resulting in selectivity indices >180 and >915 relative to its 2-day and 5-day LC50 values. Overall, this study demonstrates that contrastive graph learning can move beyond retrospective larvicide modeling to experimentally validated hit discovery, identifying LC-79 as a potent and preliminarily selective acylthiourea larvicide candidate for further mechanism-of-action, resistance, and semi-field evaluation.