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Summary Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. Availability and implementation MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Ectopically expressed olfactory receptors (ORs) have been linked with multiple clinically-relevant physiological processes. Previously used tissue-level expression estimation largely shadowed the potential role of ORs due to their overall low expression levels. Even after the introduction of the single-cell transcriptomics, a comprehensive delineation of expression dynamics of ORs in tumors remained unexplored. Our targeted investigation into single malignant cells revealed a complex landscape of combinatorial OR expression events. We observed differentiation-dependent decline in expressed OR counts per cell as well as their expression intensities in malignant cells. Further, we constructed expression signatures based on a large spectrum of ORs and tracked their enrichment in bulk expression profiles of tumor samples from The Cancer Genome Atlas (TCGA). TCGA tumor samples stratified based on OR-centric signatures exhibited divergent survival probabilities. In summary, our comprehensive analysis positions ORs at the cross-road of tumor cell differentiation status and cancer prognosis. Through analyses of olfactory receptors in single-cell RNA sequencing dataset from different cancers, Kalra, Mittal et al. find that the number and expression levels of olfactory receptors vary by differentiation status. The study provides insights into an under-appreciated role for olfactory receptors in cancer pathogenesis. [ABSTRACT FROM AUTHOR]