Discovery of novel dual adenosine A(1)/A(2A) receptor antagonists using deep learning, pharmacophore modeling and molecular docking

Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson’s disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A(1) and A(2A) receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding assay); of these hits, two compounds (C8 and C9) with 1,2,4-triazole scaffolds possessing the most potent binding affinity and antagonistic activity for A(1)/A(2A) ARs at the nanomolar level (pK(i) of 7.16–7.49 and pIC(50) of 6.31–6.78) were identified. Further molecular dynamics (MD) simulations suggested similarly strong binding interactions of the complexes between the A(1)/A(2A) ARs and two compounds (C8 and C9). Notably, the 1,2,4-triazole derivatives (compounds C8 and C9) were identified as the most potent dual A(1)/A(2A) AR antagonists in our study and could serve as a basis for further development. The effective multistage screening approach developed in this study can be utilized to identify potent ligands for other drug targets.