Bridging odorants and olfactory perception through machine learning: A review

Background: In the field of human olfactory perception (OP) and odorant chemistry (OC), a substantial corpus of data has been amassed, with efforts directed towards constructing empirically valid and applicable models. Machine learning (ML) is highly adept at processing vast quantities of data and generating sophisticated models. Recently, there has been a surge of interest in the potential of ML to bridge the gap between OC and OP. Scope and approach: This review presents a brief discussion on the application of ML to OC scenarios, accompanied by an overview of the most commonly used models. It highlights the selected input information, outlining the application of ML in OC in terms of data variability. Key findings and conclusions: While predictive models based on standardized data are valuable in odor quantification, their applicability is limited in mixed compounds. The incorporation of diagrams and spatial attributes expands the odor space, and models generated from instrumentally acquired data are primarily utilized for the differentiation of odor types. Odor descriptions, which are often directly used to influence model generation, subjective and voluminous, necessitating a process of interpretation. Meanwhile, objective data do not provide a comprehensive description of odor. This paper illustrates the future of odor prediction, offering insights into the evolving landscape of this field. It is anticipated that ML will be employed in the future to facilitate a more profound comprehension of odorants and the human olfactory sensory mechanisms, thereby offering a valuable contribution to the field.