English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The identification of protein-ligand interaction plays a key role inbiochemical research and drug discovery. Although deep learning has recentlyshown great promise in discovering new drugs, there remains a gap between deeplearning-based and experimental approaches. Here we propose a novel framework,named AIMEE, integrating AI Model and Enzymology Experiments, to identifyinhibitors against 3CL protease of SARS-CoV-2, which has taken a significanttoll on people across the globe. From a bioactive chemical library, we haveconducted two rounds of experiments and identified six novel inhibitors with ahit 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 chemicalproperties. Based on this knowledge, a commercially available compound wasselected and proven to be an activity-based probe of 3CLpro. This workhighlights the great potential of combining deep learning models andbiochemical experiments for intelligent iteration and expanding the boundariesof drug discovery.

briefings in bioinformatics

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