Molecule Generation and Optimization for Efficient Fragrance Creation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914685098393600 |
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| author | Rodrigues, Bruno C. L. Santana, Vinicius V. Murins, Sandris Nogueira, Idelfonso B. R. |
| author_facet | Rodrigues, Bruno C. L. Santana, Vinicius V. Murins, Sandris Nogueira, Idelfonso B. R. |
| contents | This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Molecule Generation and Optimization for Efficient Fragrance Creation Rodrigues, Bruno C. L. Santana, Vinicius V. Murins, Sandris Nogueira, Idelfonso B. R. Chemical Physics Machine Learning This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data. |
| title | Molecule Generation and Optimization for Efficient Fragrance Creation |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2402.12134 |