Abstract
The A2A adenosine receptor (A2AR) is emerging as a promising drug target for cancer immunotherapy. Novel A2AR antagonists are highly demanded due to few candidates entering clinic trials specific for cancer treatment. Structure-based virtual screening has made a great contribution to discover novel A2AR antagonists, but most depended on inefficient molecular docking on relatively small molecular databases. In this work, a deep learning strategy was applied to accelerate docking-based virtual screening, through which new structural types of A2AR antagonists for an extremely large molecular library were found successfully.
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