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10.1021/acs.jcim.0c00517 http://scihub22266oqcxt.onion/10.1021/acs.jcim.0c00517 32845150!ä!32845150 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Chem+Inf+Model 2020 ; 60 (10): 4582-4593 Nephropedia Template TP {{tp|p=32845150|t=2020. De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization |pdf=|usr=}}{{32845150}} gab.com Text De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization JO: J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=32845150 #FOAvip Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information. Twit Text FOAVip De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization ????J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=32845150 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32845150 #FOAvip English Wikipedia <ref>{{Cite pmid|32845150}}</ref> De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization #MMPMID32845150 Domenico A; Nicola G; Daniela T; Fulvio C; Nicola A; Orazio N J Chem Inf Model 2020[Oct]; 60 (10): 4582-4593 PMID32845150show ga Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information. |*Drug Design[MESH] |*Neural Networks, Computer[MESH] |Acetylcholinesterase/metabolism[MESH] |Antiviral Agents/chemistry/pharmacology[MESH] |Artificial Intelligence[MESH] |Betacoronavirus/enzymology[MESH] |COVID-19[MESH] |Cholinesterase Inhibitors/chemistry/pharmacology[MESH] |Coronavirus 3C Proteases[MESH] |Coronavirus Infections/drug therapy[MESH] |Cysteine Endopeptidases/metabolism[MESH] |Enzyme Inhibitors/*chemistry/*pharmacology[MESH] |Humans[MESH] |Influenza A virus/enzymology[MESH] |Molecular Docking Simulation[MESH] |Neuraminidase/antagonists & inhibitors/metabolism[MESH] |Pandemics[MESH] |Pneumonia, Viral/drug therapy[MESH] |SARS-CoV-2[MESH] |Small Molecule Libraries/chemistry/pharmacology[MESH]De Novo Drug Design of Targeted Chemical Libraries Based on Artificial(epmc).pdf DeepDyve Pubget Overpricing