Predictive Machine Learning Models for Olfaction.

A classical problem in neuroscience, biology, and chemistry is linking the chemical structure of odorants to their olfactory perception. This difficulty arises from the subjective nature of odor perception, incomplete understanding of the physiological mechanisms involved, and the absence of standardized odor descriptions. Machine learning and other computational approaches have recently been applied to tackle this challenge. This chapter presents a comprehensive workflow for constructing machine learning models for odor prediction, covering everything from problem formulation to model evaluation and real-world deployment. We also delve into recent advancements to enhance and interpret data-driven predictions while acknowledging the current limitations. The methodology outlined here offers a valuable framework for synthetic chemists and data scientists, enabling them to address the broader issue of olfaction and cater to specific needs within the fragrance and perfume industries.