Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A_2B receptor antagonists

Abstract

The Adenosine A_2B receptor (A_2BAR) is considered a novel potential target for the immunotherapy of cancer, and A_2BAR 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_2BAR. 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_2BAR antagonists. By utilizing a generative model with reported A_2BAR 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_2BAR 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_2BAR and showed preferred draggability, providing future potent development of selective A_2BAR antagonists.

Communicated by Ramaswamy H. Sarma

Keywords:

• Deep learning
• virtual screening
• adenosine receptor
• benzimidazole-pyrazine
• antagonists

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The original datasets and other supplemental data that support the results are openly available at Zenodo: https://doi.org/10.5281/zenodo.8318722

Additional information

Funding

This work has been supported by the National Key Research and Development Program of China [2023YFF1204900, 2023YFF1204902], National Natural Science Foundation of China [82003651], Guangzhou Basic and Applied Basic Research Project [202201011795] and Guangdong-Hong Kong Technology Cooperation Funding Scheme [2023A0505010015].