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10.1021/acsomega.1c00477 http://scihub22266oqcxt.onion/10.1021/acsomega.1c00477 34056406!8154149!34056406     free     free     free Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ACS+Omega 2021 ; 6 (19): 12557-12566 Nephropedia Template TP {{tp|p=34056406|t=2021. Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19 |pdf=|usr=}}{{34056406}} gab.com Text Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19 JO: ACS Omega http://www.kidney.de/pget.php?&tw=1&pmid=34056406 #FOAvip An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several ( approximately 100's) new therapeutic agents that outperform Food and Drug Administration (FDA) ( approximately 1000's) and non-FDA molecules ( approximately million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality. Twit Text FOAVip Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19 ????ACS Omega http://www.kidney.de/pget.php?&tw=1&pmid=34056406 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34056406 #FOAvip English Wikipedia <ref>{{Cite pmid|34056406}}</ref> Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19 #MMPMID34056406 Srinivasan S; Batra R; Chan H; Kamath G; Cherukara MJ; Sankaranarayanan SKRS ACS Omega 2021[May]; 6 (19): 12557-12566 PMID34056406show ga An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several ( approximately 100's) new therapeutic agents that outperform Food and Drug Administration (FDA) ( approximately 1000's) and non-FDA molecules ( approximately million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality. äArtificial Intelligence-Guided De Novo Molecular Design Targeting COVI(epmc).pdf DeepDyve Pubget Overpricing