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Nova · Professor Researcher · re-ranking top 20…
Joel D. Mainland

Joel D. Mainland

· Ph.D.Verified

University of Pennsylvania · Neuroscience

Active 2001–2024

h-index43
Citations7.9k
Papers11745 last 5y
Funding$50.7M1 active
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Research topics

  • Artificial Intelligence
  • Computer Science
  • Theoretical computer science
  • Psychology
  • Machine Learning
  • Neuroscience

Selected publications

  • A principal odor map unifies diverse tasks in olfactory perception

    Science · 2023 · 196 citations

    • Artificial Intelligence
    • Computer Science
    • Artificial Intelligence

    Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

  • A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

    bioRxiv (Cold Spring Harbor Laboratory) · 2022 · 14 citations

    • Artificial Intelligence
    • Computer Science
    • Artificial Intelligence

    Abstract Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors. One-Sentence Summary An odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.

Recent grants

Frequent coauthors

  • Hiroaki Matsunami

    Duke University

    104 shared
  • Luís R. Saraiva

    Hamad bin Khalifa University

    51 shared
  • Richard C. Gerkin

    Arizona State University

    36 shared
  • Darren W. Logan

    27 shared
  • Casey Trimmer

    23 shared
  • Johan N. Lundström

    Karolinska Institutet

    23 shared
  • Leslie B. Vosshall

    Rockefeller University

    20 shared
  • Qiuyi Chi

    Duke University

    17 shared
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