OdoriFy: A conglomerate of Artificial Intelligence-driven prediction engines for olfactory decoding

The molecular mechanisms of olfaction, or the sense of smell, are relatively under-explored compared to other sensory systems, primarily due to its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a webserver featuring powerful deep neural network-based prediction engines. OdoriFy enables 1) identification of odorant molecules for wild-type or mutant human odorant receptors (Odor Finder); 2) classification of user-provided chemicals as odorants/non-odorants (Odorant Predictor); 3) identification of responsive odorant receptors for a query odorant (OR Finder); and 4) Interaction validation using Odorant-OR Pair Analysis. Additionally, OdoriFy provides the rationale behind every prediction it makes by leveraging Explainable Artificial Intelligence. This module highlights the basis of the prediction of odorants/non-odorants at atomic resolution and for the odorant receptors at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human odorant receptors with their known agonists and non-agonists, making it a highly interactive and resource-enriched webserver. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.</p