Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376273651> ?p ?o ?g. }
- W4376273651 endingPage "4284" @default.
- W4376273651 startingPage "4261" @default.
- W4376273651 abstract "The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of −6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate." @default.
- W4376273651 created "2023-05-13" @default.
- W4376273651 creator A5006863605 @default.
- W4376273651 creator A5019980204 @default.
- W4376273651 creator A5052000306 @default.
- W4376273651 creator A5061189545 @default.
- W4376273651 creator A5069802599 @default.
- W4376273651 creator A5082353932 @default.
- W4376273651 creator A5088627070 @default.
- W4376273651 creator A5091929740 @default.
- W4376273651 date "2023-05-12" @default.
- W4376273651 modified "2023-10-18" @default.
- W4376273651 title "De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein" @default.
- W4376273651 cites W1482971402 @default.
- W4376273651 cites W1653739410 @default.
- W4376273651 cites W1975875968 @default.
- W4376273651 cites W1979683091 @default.
- W4376273651 cites W1985588649 @default.
- W4376273651 cites W1995808589 @default.
- W4376273651 cites W1997865285 @default.
- W4376273651 cites W2000815209 @default.
- W4376273651 cites W2009423060 @default.
- W4376273651 cites W2012400292 @default.
- W4376273651 cites W2027423337 @default.
- W4376273651 cites W2046412723 @default.
- W4376273651 cites W2060079863 @default.
- W4376273651 cites W2096234478 @default.
- W4376273651 cites W2096541451 @default.
- W4376273651 cites W2102377211 @default.
- W4376273651 cites W2107158607 @default.
- W4376273651 cites W2109872885 @default.
- W4376273651 cites W2125079066 @default.
- W4376273651 cites W2140247386 @default.
- W4376273651 cites W2153228625 @default.
- W4376273651 cites W2157108319 @default.
- W4376273651 cites W2222589778 @default.
- W4376273651 cites W2330799739 @default.
- W4376273651 cites W2593436234 @default.
- W4376273651 cites W2801088198 @default.
- W4376273651 cites W2898210859 @default.
- W4376273651 cites W2980377058 @default.
- W4376273651 cites W3007643904 @default.
- W4376273651 cites W3008370796 @default.
- W4376273651 cites W3009335299 @default.
- W4376273651 cites W3009912996 @default.
- W4376273651 cites W3035265089 @default.
- W4376273651 cites W3039063499 @default.
- W4376273651 cites W3045600629 @default.
- W4376273651 cites W3046463683 @default.
- W4376273651 cites W3092503590 @default.
- W4376273651 cites W3102920585 @default.
- W4376273651 cites W3119453605 @default.
- W4376273651 cites W3122987935 @default.
- W4376273651 cites W3130583627 @default.
- W4376273651 cites W3131122732 @default.
- W4376273651 cites W3133390774 @default.
- W4376273651 cites W3134093711 @default.
- W4376273651 cites W3135758766 @default.
- W4376273651 cites W3155123963 @default.
- W4376273651 cites W3178466543 @default.
- W4376273651 cites W3180196270 @default.
- W4376273651 cites W3183193919 @default.
- W4376273651 cites W3183437698 @default.
- W4376273651 cites W3199974613 @default.
- W4376273651 cites W3205700591 @default.
- W4376273651 cites W3211185008 @default.
- W4376273651 cites W4205677618 @default.
- W4376273651 cites W4210702584 @default.
- W4376273651 cites W4210860040 @default.
- W4376273651 cites W4212915778 @default.
- W4376273651 cites W4220709353 @default.
- W4376273651 cites W4225154777 @default.
- W4376273651 cites W4225313493 @default.
- W4376273651 cites W4232774153 @default.
- W4376273651 cites W4291318758 @default.
- W4376273651 cites W4292148086 @default.
- W4376273651 cites W4292559664 @default.
- W4376273651 cites W4296740991 @default.
- W4376273651 cites W4306740420 @default.
- W4376273651 cites W4307468223 @default.
- W4376273651 cites W4312173719 @default.
- W4376273651 cites W4312180920 @default.
- W4376273651 doi "https://doi.org/10.3390/cimb45050271" @default.
- W4376273651 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37232740" @default.
- W4376273651 hasPublicationYear "2023" @default.
- W4376273651 type Work @default.
- W4376273651 citedByCount "0" @default.
- W4376273651 crossrefType "journal-article" @default.
- W4376273651 hasAuthorship W4376273651A5006863605 @default.
- W4376273651 hasAuthorship W4376273651A5019980204 @default.
- W4376273651 hasAuthorship W4376273651A5052000306 @default.
- W4376273651 hasAuthorship W4376273651A5061189545 @default.
- W4376273651 hasAuthorship W4376273651A5069802599 @default.
- W4376273651 hasAuthorship W4376273651A5082353932 @default.
- W4376273651 hasAuthorship W4376273651A5088627070 @default.
- W4376273651 hasAuthorship W4376273651A5091929740 @default.
- W4376273651 hasBestOaLocation W43762736511 @default.
- W4376273651 hasConcept C147597530 @default.