In Silico Strategies for Prospective Drug Repositionings

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 a...

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Other Authors: Udrescu, Lucreția (Editor), Kurunczi, Ludovic (Editor), Bogdan, Paul (Editor), Udrescu, Mihai (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
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Online Access:DOAB: download the publication
DOAB: description of the publication
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700 1 |a Bogdan, Paul  |4 edt 
700 1 |a Udrescu, Mihai  |4 edt 
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700 1 |a Bogdan, Paul  |4 oth 
700 1 |a Udrescu, Mihai  |4 oth 
245 1 0 |a In Silico Strategies for Prospective Drug Repositionings 
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520 |a 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. 
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653 |a COVID-19 
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653 |a topological data analysis 
653 |a persistent Betti function 
653 |a SARS-CoV-2 
653 |a network-based pharmacology 
653 |a combination therapy 
653 |a nucleoside GS-441524 
653 |a fluoxetine 
653 |a synergy 
653 |a antidepressant 
653 |a natural compounds 
653 |a QSAR 
653 |a molecular docking 
653 |a drug repositioning 
653 |a UK Biobank 
653 |a vaccine 
653 |a LC-2/ad cell line 
653 |a drug discovery 
653 |a docking 
653 |a MM-GBSA calculation 
653 |a molecular dynamics 
653 |a cytotoxicity assay 
653 |a GWAS 
653 |a multiple sclerosis 
653 |a oxidative stress 
653 |a repurposing 
653 |a ADME-Tox 
653 |a bioinformatics 
653 |a complex network analysis 
653 |a modularity clustering 
653 |a ATC code 
653 |a hidradenitis suppurativa 
653 |a acne inversa 
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653 |a proteome 
653 |a comorbid disorder 
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653 |a druggable gene 
653 |a drug-repositioning 
653 |a MEK inhibitor 
653 |a MM/GBSA 
653 |a Glide docking 
653 |a MD simulation 
653 |a MM/PBSA 
653 |a single-cell RNA sequencing 
653 |a pulmonary fibrosis 
653 |a biological networks 
653 |a p38α MAPK 
653 |a allosteric inhibitors 
653 |a in silico screening 
653 |a computer-aided drug discovery 
653 |a network analysis 
653 |a psychiatric disorders 
653 |a medications 
653 |a psychiatry 
653 |a mental disorders 
653 |a toxoplasmosis 
653 |a Toxoplasma gondii 
653 |a in vitro screening 
653 |a drug targets 
653 |a drug-disease interaction 
653 |a target-disease interaction 
653 |a DPP4 inhibitors 
653 |a lipid rafts 
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