Molecule Generation and Optimization for Efficient Fragrance Creation
<|reference_start|>Molecule Generation and Optimization for Efficient Fragrance Creation: 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.<|reference_end|>
@article{rodrigues2024molecule,
    title={Molecule Generation and Optimization for Efficient Fragrance Creation},
    author={Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins and
  Idelfonso B. R. Nogueira},
    journal={arXiv preprint arXiv:2402.12134},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12134},
    primaryClass={physics.chem-ph cs.LG}
}