# Molecular Odor Prediction Based on Multi-Feature Graph Attention Networks

arXiv: https://arxiv.org/abs/2502.01430 · HTML: https://mlsanity.com/paper/2502.01430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules](https://mlsanity.com/paper/1910.10685/) (2019)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Classifier Chain Networks for Multi-Label Classification](https://mlsanity.com/paper/2411.02638/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contribution for Molecular Odor Prediction](https://mlsanity.com/paper/2505.00290/) (2025)

## Abstract (arXiv)

Olfactory perception plays a critical role in both human and organismal interactions, yet understanding of its underlying mechanisms and influencing factors remain insufficient. Molecular structures influence odor perception through intricate biochemical interactions, and accurately quantifying structure-odor relationships presents significant challenges. The Quantitative Structure-Odor Relationship (QSOR) task, which involves predicting the associations between molecular structures and their corresponding odors, seeks to address these challenges. To this end, we propose a method for QSOR, utilizing Graph Attention Networks to model molecular structures and capture both local and global features. Unlike conventional QSOR approaches reliant on predefined descriptors, our method leverages diverse molecular feature extraction techniques to automatically learn comprehensive representations. This integration enhances the model's capacity to handle complex molecular information, improves prediction accuracy. Our approach demonstrates clear advantages in QSOR prediction tasks, offering valuable insights into the application of deep learning in cheminformatics.