  The identification of protein-ligand interaction plays a key role in
biochemical research and drug discovery. Although deep learning has recently
shown great promise in discovering new drugs, there remains a gap between deep
learning-based and experimental approaches. Here we propose a novel framework,
named AIMEE, integrating AI Model and Enzymology Experiments, to identify
inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant
toll on people across the globe. From a bioactive chemical library, we have
conducted two rounds of experiments and identified six novel inhibitors with a
hit rate of 29.41%, and four of them showed an IC50 value less than 3 {\mu}M.
Moreover, we explored the interpretability of the central model in AIMEE,
mapping the deep learning extracted features to domain knowledge of chemical
properties. Based on this knowledge, a commercially available compound was
selected and proven to be an activity-based probe of 3CLpro. This work
highlights the great potential of combining deep learning models and
biochemical experiments for intelligent iteration and expanding the boundaries
of drug discovery.
