Molecule Generation and Optimization for Efficient Fragrance Creation
Bruno C. L. Rodrigues, Vinícius V. Santana, Sandris Murins, Idelfonso B. R. Nogueira
Abstract
High Resolution Image Download MS PowerPoint Slide This research introduces a machine learning-centric approach to replicate olfactory experiences, utilizing an experimentally quantified target scent from the literature as a case study for validation. 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 refinement of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamically 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.