Molecule Generation and Optimization for Efficient Fragrance Creation

• URL: http://arxiv.org/abs/2402.12134v1
• Date: Mon, 19 Feb 2024 13:32:30 GMT
• Title: Molecule Generation and Optimization for Efficient Fragrance Creation
• Authors: Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins and Idelfonso B. R. Nogueira
• Abstract summary: This research introduces a Machine Learning-centric approach to replicate olfactory experiences. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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