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• W4313367975 endingPage "106449" @default.
• W4313367975 startingPage "106449" @default.
• W4313367975 abstract "The main (Mpro) and papain-like (PLpro) proteases are highly conserved viral proteins essential for replication of the COVID-19 virus, SARS-COV-2. Therefore, a logical plan for producing new drugs against this pathogen is to discover inhibitors of these enzymes. Accordingly, the goal of the present work was to devise a computational approach to design, characterize, and select compounds predicted to be potent dual inhibitors – effective against both Mpro and PLpro. The first step employed LigDream, an artificial neural network, to create a virtual ligand library. Ligands with computed ADMET profiles indicating drug-like properties and low mammalian toxicity were selected for further study. Initial docking of these ligands into the active sites of Mpro and PLpro was done with GOLD, and the highest-scoring ligands were redocked with AutoDock Vina to determine binding free energies (ΔG). Compounds 89–00, 89–07, 89–32, and 89–38 exhibited favorable ΔG values for Mpro (−7.6 to −8.7 kcal/mol) and PLpro (−9.1 to −9.7 kcal/mol). Global docking of selected compounds with the Mpro dimer identified prospective allosteric inhibitors 89–00, 89–27, and 89–40 (ΔG -8.2 to −8.9 kcal/mol). Molecular dynamics simulations performed on Mpro and PLpro active site complexes with the four top-scoring ligands from Vina demonstrated that the most stable complexes were formed with compounds 89–32 and 89–38. Overall, the present computational strategy generated new compounds with predicted drug-like characteristics, low mammalian toxicity, and high inhibitory potencies against both target proteases to form stable complexes. Further preclinical studies will be required to validate the in silico findings before the lead compounds could be considered for clinical trials." @default.
• W4313367975 created "2023-01-06" @default.
• W4313367975 creator A5015968517 @default.
• W4313367975 creator A5019042570 @default.
• W4313367975 creator A5047948467 @default.
• W4313367975 creator A5051312286 @default.
• W4313367975 date "2023-02-01" @default.
• W4313367975 modified "2023-10-13" @default.
• W4313367975 title "SARS-CoV-2 proteases Mpro and PLpro: Design of inhibitors with predicted high potency and low mammalian toxicity using artificial neural networks, ligand-protein docking, molecular dynamics simulations, and ADMET calculations" @default.
• W4313367975 cites W1563650227 @default.
• W4313367975 cites W1963514652 @default.
• W4313367975 cites W1970232220 @default.
• W4313367975 cites W1970799005 @default.
• W4313367975 cites W1976499671 @default.
• W4313367975 cites W1981064967 @default.
• W4313367975 cites W1988437166 @default.
• W4313367975 cites W2013257331 @default.
• W4313367975 cites W2022879707 @default.
• W4313367975 cites W2027065525 @default.
• W4313367975 cites W2027257947 @default.
• W4313367975 cites W2028046413 @default.
• W4313367975 cites W2035687084 @default.
• W4313367975 cites W2037535298 @default.
• W4313367975 cites W2056022472 @default.
• W4313367975 cites W2057987200 @default.
• W4313367975 cites W2099768941 @default.
• W4313367975 cites W2105668062 @default.
• W4313367975 cites W2125976303 @default.
• W4313367975 cites W2132629607 @default.
• W4313367975 cites W2134967712 @default.
• W4313367975 cites W2144288821 @default.
• W4313367975 cites W2147993766 @default.
• W4313367975 cites W2154792548 @default.
• W4313367975 cites W2169678694 @default.
• W4313367975 cites W2186475822 @default.
• W4313367975 cites W2253523184 @default.
• W4313367975 cites W2404280981 @default.
• W4313367975 cites W241954630 @default.
• W4313367975 cites W2470646526 @default.
• W4313367975 cites W2527718275 @default.
• W4313367975 cites W2604923141 @default.
• W4313367975 cites W2610550630 @default.
• W4313367975 cites W2623172571 @default.
• W4313367975 cites W2750193626 @default.
• W4313367975 cites W2768064447 @default.
• W4313367975 cites W2890113953 @default.
• W4313367975 cites W2894601800 @default.
• W4313367975 cites W2914975766 @default.
• W4313367975 cites W2917868259 @default.
• W4313367975 cites W2918370785 @default.
• W4313367975 cites W2946293771 @default.
• W4313367975 cites W2953003901 @default.
• W4313367975 cites W3016296419 @default.
• W4313367975 cites W3016722909 @default.
• W4313367975 cites W3017082821 @default.
• W4313367975 cites W3021411957 @default.
• W4313367975 cites W3025838913 @default.
• W4313367975 cites W3047500036 @default.
• W4313367975 cites W3081089847 @default.
• W4313367975 cites W3082311967 @default.
• W4313367975 cites W3088604570 @default.
• W4313367975 cites W3094827588 @default.
• W4313367975 cites W3119119048 @default.
• W4313367975 cites W311927316 @default.
• W4313367975 cites W3119681108 @default.
• W4313367975 cites W3123245089 @default.
• W4313367975 cites W3126232912 @default.
• W4313367975 cites W3134808945 @default.
• W4313367975 cites W3141439967 @default.
• W4313367975 cites W3148453093 @default.
• W4313367975 cites W3148554359 @default.
• W4313367975 cites W3154090301 @default.
• W4313367975 cites W3154258817 @default.
• W4313367975 cites W3155123963 @default.
• W4313367975 cites W3164056060 @default.
• W4313367975 cites W3164630315 @default.
• W4313367975 cites W3165032628 @default.
• W4313367975 cites W3180743583 @default.
• W4313367975 cites W3185383228 @default.
• W4313367975 cites W3200045630 @default.
• W4313367975 cites W3201960424 @default.
• W4313367975 cites W3207670255 @default.
• W4313367975 cites W4205218078 @default.
• W4313367975 cites W4210332517 @default.
• W4313367975 cites W4212891259 @default.
• W4313367975 cites W4214673699 @default.
• W4313367975 cites W4221113661 @default.
• W4313367975 cites W4226034643 @default.
• W4313367975 cites W4226343483 @default.
• W4313367975 cites W4280559774 @default.
• W4313367975 cites W4283705078 @default.
• W4313367975 doi "https://doi.org/10.1016/j.compbiomed.2022.106449" @default.
• W4313367975 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36586228" @default.
• W4313367975 hasPublicationYear "2023" @default.
• W4313367975 type Work @default.
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