Timezone: »
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 chemoinformatics 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.
Author Information
Jennifer Wei (Google Research)
Marnix Vlot
Benjamin Sanchez-Lengeling (Google Research)
Brian Lee (Google)
Luuk Berning
Martijn Vos
Rob Henderson
Wesley Qian (Google)
D. Michael Ando
Kurt Groetsch
Richard Gerkin
Alexander Wiltschko
Koen Dechering
More from the Same Authors
-
2023 Workshop: AI for Accelerated Materials Design (AI4Mat-2023) »
Santiago Miret · Benjamin Sanchez-Lengeling · Jennifer Wei · Vineeth Venugopal · Marta Skreta · N M Anoop Krishnan -
2022 Workshop: AI for Accelerated Materials Design (AI4Mat) »
Santiago Miret · Marta Skreta · Zamyla Morgan-Chan · Benjamin Sanchez-Lengeling · Shyue Ping Ong · Alan Aspuru-Guzik -
2020 : Invited Talk: Benjamin Sanchez-Lengeling - Evaluating Attribution of Molecules with Graph Neural Networks »
Benjamin Sanchez-Lengeling -
2020 Workshop: Machine Learning for Molecules »
José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei -
2020 Poster: Evaluating Attribution for Graph Neural Networks »
Benjamin Sanchez-Lengeling · Jennifer Wei · Brian Lee · Emily Reif · Peter Wang · Wesley Qian · Kevin McCloskey · Lucy Colwell · Alexander Wiltschko -
2019 : Morning Coffee Break & Poster Session »
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger -
2019 : Molecules and Genomes »
David Haussler · Djork-Arné Clevert · Michael Keiser · Alan Aspuru-Guzik · David Duvenaud · David Jones · Jennifer Wei · Alexander D'Amour -
2018 : Invited Talk Session 3 »
Alexandre Tkatchenko · Tommi Jaakkola · Jennifer Wei