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10.1021/acs.jcim.1c00746 http://scihub22266oqcxt.onion/10.1021/acs.jcim.1c00746 34633194!ä!34633194 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Chem+Inf+Model 2021 ; 61 (11): 5589-5600 Nephropedia Template TP {{tp|p=34633194|t=2021. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking |pdf=|usr=}}{{34633194}} gab.com Text Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking JO: J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34633194 #FOAvip Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (M(pro)) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics. Twit Text FOAVip Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking ????J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34633194 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34633194 #FOAvip English Wikipedia <ref>{{Cite pmid|34633194}}</ref> Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking #MMPMID34633194 Xu Z; Wauchope OR; Frank AT J Chem Inf Model 2021[Nov]; 61 (11): 5589-5600 PMID34633194show ga Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (M(pro)) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics. |*Artificial Intelligence[MESH] |*COVID-19[MESH] |Antiviral Agents[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH] |Protease Inhibitors[MESH]Navigating Chemical Space by Interfacing Generative Artificial Intelli(epmc).pdf DeepDyve Pubget Overpricing