In Silico Strategies for Prospective Drug Repositionings
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| Autore: |
Udrescu Lucreția
|
| Titolo: |
In Silico Strategies for Prospective Drug Repositionings
|
| Pubblicazione: | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica: | 1 electronic resource (288 p.) |
| Soggetto topico: | Medicine |
| Pharmaceutical industries | |
| Soggetto non controllato: | COVID-19 |
| drug repurposing | |
| topological data analysis | |
| persistent Betti function | |
| SARS-CoV-2 | |
| network-based pharmacology | |
| combination therapy | |
| nucleoside GS-441524 | |
| fluoxetine | |
| synergy | |
| antidepressant | |
| natural compounds | |
| QSAR | |
| molecular docking | |
| drug repositioning | |
| UK Biobank | |
| vaccine | |
| LC-2/ad cell line | |
| drug discovery | |
| docking | |
| MM-GBSA calculation | |
| molecular dynamics | |
| cytotoxicity assay | |
| GWAS | |
| multiple sclerosis | |
| oxidative stress | |
| repurposing | |
| ADME-Tox | |
| bioinformatics | |
| complex network analysis | |
| modularity clustering | |
| ATC code | |
| hidradenitis suppurativa | |
| acne inversa | |
| transcriptome | |
| proteome | |
| comorbid disorder | |
| biomarker | |
| signaling pathway | |
| druggable gene | |
| drug-repositioning | |
| MEK inhibitor | |
| MM/GBSA | |
| Glide docking | |
| MD simulation | |
| MM/PBSA | |
| single-cell RNA sequencing | |
| pulmonary fibrosis | |
| biological networks | |
| p38α MAPK | |
| allosteric inhibitors | |
| in silico screening | |
| computer-aided drug discovery | |
| network analysis | |
| psychiatric disorders | |
| medications | |
| psychiatry | |
| mental disorders | |
| toxoplasmosis | |
| Toxoplasma gondii | |
| in vitro screening | |
| drug targets | |
| drug-disease interaction | |
| target-disease interaction | |
| DPP4 inhibitors | |
| lipid rafts | |
| Persona (resp. second.): | KuruncziLudovic |
| BogdanPaul | |
| UdrescuMihai | |
| UdrescuLucreția | |
| Sommario/riassunto: | The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions. |
| Titolo autorizzato: | In Silico Strategies for Prospective Drug Repositionings |
| ISBN: | 3-0365-6133-1 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910639987703321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |