Department of Chemical Engineering, Indian Institute of Technology (Banaras Hindu University, Varanasi, U.P., 221005, India.
Department of Chemistry, Indian Institute of Technology (Banaras Hindu University), Varanasi, U.P., 221005, India.
Sci Rep. 2022 Aug 16;12(1):13863. doi: 10.1038/s41598-022-18086-y.
Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure-odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.
长期以来，解码嗅觉感知背后的因素一直是人类神经科学、嗅觉研究、香水、心理学、生物学和化学领域的一个挑战。基于数据的新一波方法和机器学习方法在预测分子性质方面的应用是一个日益受到关注的研究领域，它们为传统的统计方法提供了显著的改进。我们在预测分子气味的背景下研究这些方法，特别是关注用于相同目的的多标签分类策略。具体来说，有二元相关性、分类器链和随机森林等方法。这个被称为定量构效关系的挑战，在机器学习的感官感知领域仍然是一个未解决的任务，我们希望随着时间的推移，在嗅觉领域模仿在视觉和听觉感知领域取得的成果。
