Molecule Generation and Optimization for Efficient Fragrance Creation

arXiv:2402.12134v1 Announce Type: cross
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. …

abstract arxiv cs.lg experimental generative generator graph human hybrid key machine machine learning networks neural networks optimization perception physics.chem-ph quantification replicate research through type