Molecule Generation and Optimization for Efficient Fragrance Creation

Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins, Idelfonso B. R. Nogueira
2/19/2024
physics.chem-phcs.LG

No official code found. Best implementation: BrunoCLR/MolGen_Opt (4 stars, last commit 2024-07-09).

Abstract

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.

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BrunoCLR/MolGen_OptCommunity100%

Molecule Generation and Optimization for Efficient Fragrance Creation

40Jan 15, 20242 years ago

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Cite this paper

@article{rodrigues2024molecule,
  title  = {Molecule Generation and Optimization for Efficient Fragrance Creation},
  author = {Bruno C. L. Rodrigues and Vinicius V. Santana and Sandris Murins and Idelfonso B. R. Nogueira},
  year   = {2024},
  eprint = {2402.12134},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.12134v1}
}

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