FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384339408> ?p ?o ?g. }

• W4384339408 endingPage "11" @default.
• W4384339408 startingPage "1" @default.
• W4384339408 abstract "De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets’ molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma" @default.
• W4384339408 created "2023-07-15" @default.
• W4384339408 creator A5026142822 @default.
• W4384339408 creator A5027135252 @default.
• W4384339408 creator A5040669765 @default.
• W4384339408 creator A5048635416 @default.
• W4384339408 creator A5060841142 @default.
• W4384339408 creator A5061990319 @default.
• W4384339408 creator A5062315365 @default.
• W4384339408 creator A5062740680 @default.
• W4384339408 creator A5071411811 @default.
• W4384339408 creator A5081851694 @default.
• W4384339408 date "2023-07-14" @default.
• W4384339408 modified "2023-10-15" @default.
• W4384339408 title "De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations" @default.
• W4384339408 cites W1965364481 @default.
• W4384339408 cites W1975147762 @default.
• W4384339408 cites W1986853728 @default.
• W4384339408 cites W1992156271 @default.
• W4384339408 cites W1998582365 @default.
• W4384339408 cites W1999318478 @default.
• W4384339408 cites W2009315806 @default.
• W4384339408 cites W2027478081 @default.
• W4384339408 cites W2028279608 @default.
• W4384339408 cites W2041686943 @default.
• W4384339408 cites W2044834685 @default.
• W4384339408 cites W2064675550 @default.
• W4384339408 cites W2103325328 @default.
• W4384339408 cites W2126105956 @default.
• W4384339408 cites W2137705897 @default.
• W4384339408 cites W2151697120 @default.
• W4384339408 cites W2153972688 @default.
• W4384339408 cites W2155478691 @default.
• W4384339408 cites W2164526213 @default.
• W4384339408 cites W2171830166 @default.
• W4384339408 cites W2284201926 @default.
• W4384339408 cites W2287058098 @default.
• W4384339408 cites W2318527110 @default.
• W4384339408 cites W2329046360 @default.
• W4384339408 cites W2402506318 @default.
• W4384339408 cites W2470899874 @default.
• W4384339408 cites W2610148085 @default.
• W4384339408 cites W2767711842 @default.
• W4384339408 cites W2769484073 @default.
• W4384339408 cites W2775714759 @default.
• W4384339408 cites W2783897572 @default.
• W4384339408 cites W2897701010 @default.
• W4384339408 cites W2925830236 @default.
• W4384339408 cites W2947218073 @default.
• W4384339408 cites W2950354128 @default.
• W4384339408 cites W2963042536 @default.
• W4384339408 cites W3009970748 @default.
• W4384339408 cites W3015608194 @default.
• W4384339408 cites W3040116040 @default.
• W4384339408 cites W3044906678 @default.
• W4384339408 cites W3047500036 @default.
• W4384339408 cites W3088790168 @default.
• W4384339408 cites W3088849005 @default.
• W4384339408 cites W3090171308 @default.
• W4384339408 cites W3100998741 @default.
• W4384339408 cites W3119946946 @default.
• W4384339408 cites W3126326305 @default.
• W4384339408 cites W3145734614 @default.
• W4384339408 cites W3167283626 @default.
• W4384339408 cites W3186504953 @default.
• W4384339408 cites W3203805144 @default.
• W4384339408 cites W4200354643 @default.
• W4384339408 cites W4281570112 @default.
• W4384339408 cites W4313488446 @default.
• W4384339408 doi "https://doi.org/10.1080/07391102.2023.2234481" @default.
• W4384339408 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37449757" @default.
• W4384339408 hasPublicationYear "2023" @default.
• W4384339408 type Work @default.
• W4384339408 citedByCount "1" @default.
• W4384339408 countsByYear W43843394082023 @default.
• W4384339408 crossrefType "journal-article" @default.
• W4384339408 hasAuthorship W4384339408A5026142822 @default.
• W4384339408 hasAuthorship W4384339408A5027135252 @default.
• W4384339408 hasAuthorship W4384339408A5040669765 @default.
• W4384339408 hasAuthorship W4384339408A5048635416 @default.
• W4384339408 hasAuthorship W4384339408A5060841142 @default.
• W4384339408 hasAuthorship W4384339408A5061990319 @default.
• W4384339408 hasAuthorship W4384339408A5062315365 @default.
• W4384339408 hasAuthorship W4384339408A5062740680 @default.
• W4384339408 hasAuthorship W4384339408A5071411811 @default.
• W4384339408 hasAuthorship W4384339408A5081851694 @default.
• W4384339408 hasConcept C103697762 @default.
• W4384339408 hasConcept C108583219 @default.
• W4384339408 hasConcept C121332964 @default.
• W4384339408 hasConcept C142724271 @default.
• W4384339408 hasConcept C154945302 @default.
• W4384339408 hasConcept C163258240 @default.
• W4384339408 hasConcept C167966045 @default.
• W4384339408 hasConcept C177212765 @default.
• W4384339408 hasConcept C185592680 @default.
• W4384339408 hasConcept C2779134260 @default.
• W4384339408 hasConcept C2780992000 @default.
• W4384339408 hasConcept C2910188402 @default.

• next