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.
@article{arxiv.2402.12134,
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},
journal= {arXiv preprint arXiv:2402.12134},
year = {2024}
}