A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe

TL;DR

This paper presents AIMEE, a framework combining AI modeling and enzymology experiments to identify SARS-CoV-2 3CL protease inhibitors, successfully discovering novel compounds and an activity-based probe, advancing drug discovery efforts.

Contribution

The study introduces a novel integrated framework, AIMEE, that effectively combines deep learning and experimental validation to identify inhibitors and probes for SARS-CoV-2 3CL protease.

Findings

Identified six novel inhibitors with a 29.41% hit rate.

Four inhibitors showed IC50 values below 3 μM.

Validated an activity-based probe for 3CLpro.

Abstract

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.