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

Dataset DOI: [https://doi.org/10.5061/dryad.73n5tb38b](https://doi.org/10.5061/dryad.73n5tb38b)

This dataset supports the analyses presented in the study titled \"A deep learning and digital archaeology approach for mosquito repellent discovery\". The data include experimental repellency assays conducted on ticks and mosquitoes, digitized historical datasets, and scripts for data analysis and figure generation.

Description of the data and file structure

The dataset consists of experimental data files and associated Jupyter notebooks and Python scripts used to analyze repellency data and generate the figures presented in the publication. The files are structured as follows:

\- Fig5B.csv: Contains 18 columns documenting repellency testing against Anopheles stephensi. Empty fields indicate unavailable data. Below is a detailed account of the data in these columns.

1 - Molecule name: Unique alphanumeric ...,