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10.1093/bib/bbab301 http://scihub22266oqcxt.onion/10.1093/bib/bbab301 34368837!8385923!34368837     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34368837&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Brief+Bioinform 2021 ; 22 (6): ? Nephropedia Template TP {{tp|p=34368837|t=2021. A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe |pdf=|usr=}}{{34368837}} gab.com Text A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34368837 #FOAvip 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 enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 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 <3 muM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was 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 for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE. Twit Text FOAVip A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34368837 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34368837 #FOAvip English Wikipedia <ref>{{Cite pmid|34368837}}</ref> A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe #MMPMID34368837 Hu F; Wang L; Hu Y; Wang D; Wang W; Jiang J; Li N; Yin P Brief Bioinform 2021[Nov]; 22 (6): ? PMID34368837show ga 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 enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 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 <3 muM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was 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 for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE. |*COVID-19 Drug Treatment[MESH] |Antiviral Agents/chemistry/therapeutic use[MESH] |Artificial Intelligence[MESH] |COVID-19/genetics/virology[MESH] |Drug Discovery[MESH] |Humans[MESH] |Ligands[MESH] |Protease Inhibitors/*chemistry/therapeutic use[MESH] |SARS-CoV-2/*chemistry/drug effects/pathogenicity[MESH]A novel framework integrating AI model and enzymological experiments p(epmc).pdf DeepDyve Pubget Overpricing