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10.4155/fmc-2020-0262 http://scihub22266oqcxt.onion/10.4155/fmc-2020-0262 33590764!7888348!33590764     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Future+Med+Chem 2021 ; 13 (6): 575-585 Nephropedia Template TP {{tp|p=33590764|t=2021. De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence |pdf=|usr=}}{{33590764}} gab.com Text De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence JO: Future Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33590764 #FOAvip Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2. Twit Text FOAVip De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence ????Future Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33590764 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33590764 #FOAvip English Wikipedia <ref>{{Cite pmid|33590764}}</ref> De novo design of new chemical entities for SARS-CoV-2 using artificial intelligence #MMPMID33590764 Bung N; Krishnan SR; Bulusu G; Roy A Future Med Chem 2021[Mar]; 13 (6): 575-585 PMID33590764show ga Background: The novel coronavirus SARS-CoV-2 has severely affected the health and economy of several countries. Multiple studies are in progress to design novel therapeutics against the potential target proteins in SARS-CoV-2, including 3CL protease, an essential protein for virus replication. Materials & methods: In this study we employed deep neural network-based generative and predictive models for de novo design of small molecules capable of inhibiting the 3CL protease. The generative model was optimized using transfer learning and reinforcement learning to focus around the chemical space corresponding to the protease inhibitors. Multiple physicochemical property filters and virtual screening score were used for the final screening. Conclusion: We have identified 33 potential compounds as ideal candidates for further synthesis and testing against SARS-CoV-2. |*COVID-19 Drug Treatment[MESH] |*Drug Design[MESH] |Antiviral Agents/chemistry/pharmacology[MESH] |Artificial Intelligence[MESH] |COVID-19/virology[MESH] |Coronavirus 3C Proteases/*antagonists & inhibitors/chemistry/metabolism[MESH] |Drug Discovery/methods[MESH] |Humans[MESH] |Ligands[MESH] |Molecular Docking Simulation[MESH] |Protease Inhibitors/*chemistry/*pharmacology[MESH]De novo design of new chemical entities for SARS-CoV-2 using artificia(epmc).pdf DeepDyve Pubget Overpricing