  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.
