Molecular Odor Prediction Based on Multi-Feature Graph Attention Networks Article Swipe
Huidong Xie
,
Jiande Sun
,
Yi Shao
,
Shuai Li
,
Sujuan Hou
,
Yan Sun
,
Jian Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01430
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01430
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.01430
- https://arxiv.org/pdf/2502.01430
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407170491
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407170491Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.01430Digital Object Identifier
- Title
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Molecular Odor Prediction Based on Multi-Feature Graph Attention NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-03Full publication date if available
- Authors
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Huidong Xie, Jiande Sun, Yi Shao, Shuai Li, Sujuan Hou, Yan Sun, Jian WangList of authors in order
- Landing page
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https://arxiv.org/abs/2502.01430Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2502.01430Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2502.01430Direct OA link when available
- Concepts
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Odor, Feature (linguistics), Computer science, Graph, Artificial intelligence, Pattern recognition (psychology), Machine learning, Theoretical computer science, Psychology, Neuroscience, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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