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• W4362508408 abstract "Abstract Identifying the correct chemotype of ligands targeting receptors (i.e., agonist or antagonist) is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson’s disease and cancer. The identified antagonists were tested here in a radioligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310±23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range." @default.
• W4362508408 created "2023-04-06" @default.
• W4362508408 creator A5016571029 @default.
• W4362508408 creator A5026564872 @default.
• W4362508408 creator A5030695255 @default.
• W4362508408 creator A5031575323 @default.
• W4362508408 creator A5041456754 @default.
• W4362508408 creator A5043471682 @default.
• W4362508408 creator A5076382838 @default.
• W4362508408 date "2023-04-02" @default.
• W4362508408 modified "2023-10-15" @default.
• W4362508408 title "Machine Learning-aided Discovery of Novel Chemotype Antagonists for G Protein-coupled Receptors: The Case of the Adenosine A_2AReceptor" @default.
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