{Reference Type}: Journal Article
{Title}: Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A2B receptor antagonists.
{Author}: Qin R;Zhang H;Huang W;Shao Z;Lei J;
{Journal}: J Biomol Struct Dyn
{Volume}: 0
{Issue}: 0
{Year}: 2023 Dec 22
{Factor}: 5.235
{DOI}: 10.1080/07391102.2023.2295974
{Abstract}: The Adenosine A2B receptor (A2BAR) is considered a novel potential target for the immunotherapy of cancer, and A2BAR 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 A2BAR. 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 A2BAR antagonists. By utilizing a generative model with reported A2BAR 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 A2BAR 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 A2BAR and showed preferred draggability, providing future potent development of selective A2BAR antagonists.Communicated by Ramaswamy H. Sarma.