RT Journal Article SR Electronic T1 A deep learning and digital archaeology approach for mosquito repellent discovery JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.09.01.504601 DO 10.1101/2022.09.01.504601 A1 Jennifer N. Wei A1 Marnix Vlot A1 Benjamin Sanchez-Lengeling A1 Brian K. Lee A1 Luuk Berning A1 Martijn W. Vos A1 Rob W.M. Henderson A1 Wesley W. Qian A1 D. Michael Ando A1 Kurt M. Groetsch A1 Richard C. Gerkin A1 Alexander B. Wiltschko A1 Koen J. Dechering YR 2022 UL http://biorxiv.org/content/early/2022/11/21/2022.09.01.504601.abstract AB 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 pest species 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. 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.Competing Interest StatementKJD owns stock in TropIQ.