Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A 2B receptor antagonists.

The Adenosine A 2B receptor (A 2B AR) is considered a novel potential target for the immunotherapy of cancer, and A 2B AR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of benzimidazole-pyrazine scaffolds whose derivatives exhibited the antagonistic effect but lacked subtype selectivity towards A 2B AR. In this work, we developed a scaffold-based protocol that incorporates a deep generative model and multilayer virtual screening to design benzimidazole-pyrazine derivatives as potential selective A 2B AR antagonists. By utilizing a generative model with reported A 2B AR antagonists as the training set, we built up a scaffold-focused library of benzimidazole-pyrazine derivatives and processed a virtual screening protocol to discover potential A 2B AR antagonists. Finally, five molecules with different Bemis-Murcko scaffolds were identified and exhibited higher binding free energies than the reference molecule 12o. Further computational analysis revealed that the 3-benzyl derivative ABA-1266 presented high selectivity toward A 2B AR and showed preferred draggability, providing future potent development of selective A 2B AR antagonists.Communicated by Ramaswamy H. Sarma.