Predicting odor from molecular structure: a multi-label classification approach

ABSTRACT: 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

SUBMITTER: Saini K 

PROVIDER: S-EPMC9381526 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature