Molecular Odor Prediction with Harmonic Modulated Feature Mapping and Chemically-Informed Loss Article Swipe
Huidong Xie
,
Jiande Sun
,
Yi Shao
,
Shuai Li
,
Sujuan Hou
,
Yan Sun
,
Yuxiang Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01296
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01296
Molecular odor prediction has great potential across diverse fields such as chemistry, pharmaceuticals, and environmental science, enabling the rapid design of new materials and enhancing environmental monitoring. However, current methods face two main challenges: First, existing models struggle with non-smooth objective functions and the complexity of mixed feature dimensions; Second, datasets suffer from severe label imbalance, which hampers model training, particularly in learning minority class labels. To address these issues, we introduce a novel feature mapping method and a molecular ensemble optimization loss function. By incorporating feature importance learning and frequency modulation, our model adaptively adjusts the contribution of each feature, efficiently capturing the intricate relationship between molecular structures and odor descriptors. Our feature mapping preserves feature independence while enhancing the model's efficiency in utilizing molecular features through frequency modulation. Furthermore, the proposed loss function dynamically adjusts label weights, improves structural consistency, and strengthens label correlations, effectively addressing data imbalance and label co-occurrence challenges. Experimental results show that our method significantly can improves the accuracy of molecular odor prediction across various deep learning models, demonstrating its promising potential in molecular structure representation and chemoinformatics.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.01296
- https://arxiv.org/pdf/2502.01296
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407133240
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407133240Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.01296Digital Object Identifier
- Title
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Molecular Odor Prediction with Harmonic Modulated Feature Mapping and Chemically-Informed LossWork 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, Yuxiang LiuList of authors in order
- Landing page
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https://arxiv.org/abs/2502.01296Publisher landing page
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https://arxiv.org/pdf/2502.01296Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2502.01296Direct OA link when available
- Concepts
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Odor, Feature (linguistics), Harmonic, Computer science, Psychology, Artificial intelligence, Pattern recognition (psychology), Physics, Acoustics, Neuroscience, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.dynamically | 135 |
| abstract_inverted_index.effectively | 146 |
| abstract_inverted_index.efficiently | 101 |
| abstract_inverted_index.modulation, | 91 |
| abstract_inverted_index.modulation. | 129 |
| abstract_inverted_index.monitoring. | 26 |
| abstract_inverted_index.strengthens | 143 |
| abstract_inverted_index.Experimental | 154 |
| abstract_inverted_index.Furthermore, | 130 |
| abstract_inverted_index.consistency, | 141 |
| abstract_inverted_index.contribution | 97 |
| abstract_inverted_index.descriptors. | 111 |
| abstract_inverted_index.independence | 117 |
| abstract_inverted_index.optimization | 81 |
| abstract_inverted_index.particularly | 60 |
| abstract_inverted_index.relationship | 105 |
| abstract_inverted_index.co-occurrence | 152 |
| abstract_inverted_index.correlations, | 145 |
| abstract_inverted_index.demonstrating | 174 |
| abstract_inverted_index.environmental | 14, 25 |
| abstract_inverted_index.incorporating | 85 |
| abstract_inverted_index.significantly | 160 |
| abstract_inverted_index.representation | 181 |
| abstract_inverted_index.pharmaceuticals, | 12 |
| abstract_inverted_index.chemoinformatics. | 183 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |