@article{chemsenses2025repellents,
  title = {A deep learning and digital archaeology approach for mosquito repellent discovery},
  author = {Wei, Jennifer N. and Ruiz, Carlos and Vlot, Marnix and Sanchez-Lengeling, Benjamin and Lee, Brian K. and Berning, Luuk and Vos, Martijn W. and Henderson, Rob W. M. and Qian, Wesley W. and Sanders, Jacob N. and Ando, D. Michael and Groetsch, Kurt M. and Gerkin, Richard C. and Wiltschko, Alexander B. and Riffell, Jeffrey A. and Dechering, Koen J.},
  journal = {Chemical Senses},
  volume = {50},
  pages = {bjaf021},
  year = {2025},
  month = jul,
  doi = {10.1093/chemse/bjaf021},
  url = {https://doi.org/10.1093/chemse/bjaf021},
  preprint = {https://doi.org/10.1101/2022.09.01.504601},
  github = {https://github.com/BioMachineLearning/openpom},
  keywords = {olfaction, small-molecules, graphs, ai},
  abstract = {Leverages graph neural representations from the Principal Odor Map (POM) to discover potent, novel repellent chemotypes validated experimentally against multiple mosquito disease vectors.},
}