A deep learning and digital archaeology approach for mosquito repellent discovery.

ABSTRACT: Insect-borne diseases kill > 0.5 million people annually. Currently available repellents for personal or household protection are limited in their efficacy, applicability, and safety profile. Here, we describe a machine-learning-driven high-throughput method for the discovery of novel repellent molecules. To achieve this, we digitized a large, historic dataset containing ~19,000 mosquito repellency measurements. We then trained a graph neural network (GNN) to map molecular structure and repellency. We applied this model to select 317 candidate molecules to test in parallelizable behavioral assays, quantifying repellency in multiple insect vectors of the pathogens of disease and in follow-up trials with human volunteers. The GNN approach outperformed a chemoinformatic model and produced a h

SUBMITTER: Wei JN 

PROVIDER: S-EPMC12342189 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature