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.
摘要：
腺苷A2B受体（A2BAR）被认为是癌症免疫治疗的新的潜在靶标。A2BAR拮抗剂对肿瘤生长有抑制作用，扩散,和转移。在我们之前的研究中,我们确定了一类苯并咪唑-吡嗪支架，其衍生物表现出拮抗作用，但缺乏对A2BAR的亚型选择性。在这项工作中,我们开发了一种基于支架的方案,该方案结合了深度生成模型和多层虚拟筛选,以设计苯并咪唑-吡嗪衍生物作为潜在的选择性A2BAR拮抗剂.通过利用已报道的A2BAR拮抗剂作为训练集的生成模型，我们建立了一个以支架为中心的苯并咪唑-吡嗪衍生物文库,并进行了虚拟筛选方案,以发现潜在的A2BAR拮抗剂.最后,鉴定了具有不同Bemis-Murcko支架的五种分子，并表现出比参考分子12o更高的结合自由能。进一步的计算分析表明，3-苄基衍生物ABA-1266对A2BAR具有高选择性，并显示出优选的可拖动性，提供选择性A2BAR拮抗剂的未来有效发展。由RamaswamyH.Sarma沟通。
