A deep learning and digital archaeology approach for mosquito repellent discovery

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 hit rate that increased with training
data size, suggesting that both model innovation and novel data collection
were integral to predictive accuracy. We identified >10 molecules
with repellency similar to or greater than the most widely used
repellents. We analyzed the neural responses from the mosquito antennal
(olfactory) lobe to selected repellents and found strong responses to many
of the tested compounds, including those predicted to be strong
repellents. Results from the AL recordings also demonstrated a correlation
between the evoked responses to strong repellents and our GNN
representation. This approach enables computational screening of billions
of possible molecules to identify empirically tractable numbers of
candidate repellents, leading to accelerated progress towards solving a
global health challenge.