.png){kind=icon}
Return
Interpretable Multitask Deep Learning Models for Odor Perception Based on Molecular Structure
.png){kind=icon}
OA
AI
DOI：10.1016/j.crfs.2025.101219.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Developed an interpretable multitask GNN model for odor prediction • Achieved superior accuracy over single-task and Random Forest models • Model identifies shared molecular features across multiple odor labels • Integrated Gradients revealed key substructures driving odor predictions
Keywords:
Odor prediction
Graph neural network
Olfactory receptor
Explainable AI
Multitask learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.6K
Citations:
5.3K
Organization
No organization information available
Cited Papers
ShareSaveShareSaveNature's Chemical Signatures in Human Olfaction: A Foodborne Perspective for Future Biotechnology
PREAI
ShareSaveShareSave
POP-CNN: Predicting Odor Pleasantness With Convolutional Neural Network.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
ShareSave no more
no more