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. Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system. Dual adenosine A(1)/A(2A) receptor antagonists provide a possible effective therapy that can simultaneously solve motor symptoms and nonmotor symptoms in the medical treatment of PD. Here, we applied a multistage virtual screening approach involving deep learning, pharmacophore models, and molecular docking methods to the ChemDiv library, which contains 1,178,506 compounds, and identified two 1,2,4-triazole derivatives as dual adenosine A(1) and A(2A) receptor antagonists. We used a cAMP functional assay and radioligand binding assay to evaluate the biological activity and binding affinities towards adenosine A(1) and A(2A) receptor of 8 selected compounds, respectively. The molecular dynamics (MD) simulations showed similarly strong binding interactions of the complexes between the adenosine A(1)/A(2A) receptors and the compounds. The results showed that the multistage virtual screening approach can effectively identify potent dual adenosine A(1)/A(2A) receptor antagonists. The multistage screening approach offers an alternative and effective strategy for the identification of potent ligands for other drug targets.

Automated summary

Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system. Dual adenosine A(1)/A(2A) receptor antagonists provide a possible effective therapy that can simultaneously solve motor symptoms and nonmotor symptoms in the medical treatment of PD. In this study, a multistage virtual screening approach involving deep learning, pharmacophore models, and molecular docking methods identified two 1,2,4-triazole derivatives as dual adenosine A(1) and A(2A) receptor antagonists. The results of molecular dynamics (MD) simulations showed strong binding interactions between the adenosine A(1)/A(2A) receptors and the compounds.