Molecule Generation and Optimization for Efficient Fragrance Creation
This work addresses the challenge of efficient fragrance creation for the perfume industry, representing an incremental advancement in applying machine learning to olfactory science.
The research tackled the problem of replicating olfactory experiences by developing a hybrid model that connects perfume molecular structure to human perception, validated by reproducing two distinct olfactory experiences using experimental data.
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.