Dutta Prantar, Jain Deepak, Gupta Rakesh, Rai Beena
Physical Sciences Research Area, TCS Research, Tata Consultancy Services, Pune, India.
Methods Mol Biol. 2025;2915:71-99. doi: 10.1007/978-1-0716-4466-9_4.
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.
神经科学、生物学和化学领域的一个经典问题是将气味剂的化学结构与其嗅觉感知联系起来。这一难题源于气味感知的主观性、对所涉及生理机制的不完全理解以及缺乏标准化的气味描述。机器学习和其他计算方法最近已被应用于应对这一挑战。本章介绍了一个用于构建气味预测机器学习模型的全面工作流程，涵盖从问题提出到模型评估及实际应用的方方面面。我们还深入探讨了在认识到当前局限性的同时，增强和解释数据驱动预测的最新进展。这里概述的方法为合成化学家及数据科学家提供了一个有价值的框架，使他们能够解决更广泛的嗅觉问题，并满足香料和香水行业的特定需求。
