
Joel D. Mainland
· Ph.D.VerifiedUniversity of Pennsylvania · Neuroscience
Active 2001–2024
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
Emerging Strategies for Treating Chemosensory Disorders
NIH · $40k · 2018–2019
NIH · $476k · 2013
Predicting Human Olfactory Perception from Molecular Structure
NIH · $2.0M · 2020–2025
Interdisciplinary Training in the Chemical Senses
NIH · $7.2M · 1979–2027
Predictive Computational Models of Olfactory Networks
NIH · $38.9M · 2019–2025
Frequent coauthors
- 104 shared
Hiroaki Matsunami
Duke University
- 51 shared
Luís R. Saraiva
Hamad bin Khalifa University
- 36 shared
Richard C. Gerkin
Arizona State University
- 27 shared
Darren W. Logan
- 23 shared
Casey Trimmer
- 23 shared
Johan N. Lundström
Karolinska Institutet
- 20 shared
Leslie B. Vosshall
Rockefeller University
- 17 shared
Qiuyi Chi
Duke University
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