DOI: 10.5061/dryad.73n5tb38b
title: A deep learning and digital archaeology approach for mosquito repellent discovery

description: Insect-borne diseases kill &gt;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 &gt;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.
description: # 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 identifier for each test compound. 2 - SMILES: Simplified Molecular Input Line Entry System. This column contains an ASCII string representing the chemical structure of each of the molecules tested. 3 - Collections: Provides details about batches and sets from which compounds were sourced, and information about the trials in which they were used. Columns 4 to 18 contain raw data associated with "hand to cage" behavioral assays on Anopheles stephensi, as follows: 4 - Experiment number 5 - Duration of recording in minutes 6 - Concentration of the test compound (µg/cm2) 7 - Evaporation time (Min.) 8 - Compound volume (ml) 9 - Surface (cm2) 10 - Material 11 - Volunteer name 12 - Volunteer alias 13 - Test Date 14 - Repellency (%) 15 - Average repellency (%) 16 - Average repellency (%) Standard Deviation (Â±) 17 - Average repellency (%) Count 18 - Concentration percentage (%) \- Figure5B.ipynb: Jupyter notebook for visualizing repellency performance using barplots and stripplots, color-coded by the number of unique volunteers. Requires Python 3.8.17 with matplotlib.pyplot, pandas, numpy, and seaborn. \- Fig5C.csv: Contains binary repellency outcome data for Aedes aegypti and ticks. Columns 15–24 represent repellency response variables. The column labeled ED50 contains the effective dose at which the compound tested achieves 50% repellency. ED50_binary is a binary variable that indicates whether the ED50 value is above (1) or below (0) a threshold of 1.2. \- Figure5C.ipynb: Jupyter notebook generating joint plots comparing average repellency across the two taxa. \- Fig6B.csv: Contains repellency values (column 5) for three taxa: Anopheles (column 2), Aedes (column 3), and ticks (column 4), associated with specific chemical compounds (column 1). Empty cells in this list mean that the compound in question was not tested under that particular condition. \- Figure6B.ipynb: Notebook to generate comparative plots across taxa. Uses pandas, matplotlib.pyplot, and seaborn. \- USDA_compounds.csv: Digitized list of chemical compounds used to train the neural network. Repellency values for these compounds under different conditions in Aedes aegypti and Anopheles stephensi were collected from published documents (King, 1947, 1954; USDA, 1967). \- 1947-King-USDA_dataset.csv: Moderately curated data table with repellency values on skin and clothing against yellow fever mosquitoes. Missing values are marked as N/A. \- 1954-King_dataset.csv: Moderately curated dataset including repellency on skin and clothing against yellow fever and malaria mosquitoes. Missing values are represented as '-'. The non-numeric value "4A" denotes maximum repellency, as defined by the original source. \- 1967-USDA_dataset.csv: Digitized and moderately curated repellency dataset against Aedes aegypti in multiple conditions (e.g., skin, clothing, olfactometer). Missing values from OCR are labeled as N/A, and blanks from the original are marked as '-'. \- Subplot_C_DEET_Curves.py: Script for processing calcium imaging data from the antennal lobe of Ae. aegypti. Outputs response plots. Depends on numpy, matplotlib.pyplot, os, glob, and csv. \- Subplot_C_DEET_Heatmaps.py: Script for generating heatmap visualizations of average neural activity during chemical stimulation. Uses os, glob, csv, numpy, pandas, sklearn.decomposition.PCA, and matplotlib.pyplot. Sharing/Access Information Data was derived from the following sources: \- King (1947, 1954) \- USDA (1967) Digitized and curated versions of these datasets are included in this submission. Code/Software The dataset includes Jupyter notebooks and Python scripts. All scripts were run using Python 3.8.17 and rely on the following packages: \- pandas \- numpy \- matplotlib.pyplot \- seaborn \- os \- glob \- csv \- sklearn.decomposition.PCA (for calcium imaging heatmaps) The notebooks are named by figure (e.g., Figure5B.ipynb, Figure6B.ipynb) and correspond to specific plots in the manuscript. Each notebook is self-contained and includes comments guiding the user through the data visualization process.