AUTHOR=Puhl Ana C. , Gao Zhan-Guo , Jacobson Kenneth A. , Ekins Sean TITLE=Machine Learning for Discovery of New ADORA Modulators JOURNAL=Frontiers in Pharmacology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.920643 DOI=10.3389/fphar.2022.920643 ISSN=1663-9812 ABSTRACT=
Adenosine (ADO) is an extracellular signaling molecule generated locally under conditions that produce ischemia, hypoxia, or inflammation. It is involved in modulating a range of physiological functions throughout the brain and periphery through the membrane-bound G protein-coupled receptors, called adenosine receptors (ARs) A1AR, A2AAR, A2BAR, and A3AR. These are therefore important targets for neurological, cardiovascular, inflammatory, and autoimmune diseases and are the subject of drug development directed toward the cyclic adenosine monophosphate and other signaling pathways. Initially using public data for A1AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity