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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Udrescu, Lucreția edt In Silico Strategies for Prospective Drug Repositionings Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (288 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Medicine bicssc Pharmaceutical industries bicssc 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 3-0365-6134-X Kurunczi, Ludovic edt Bogdan, Paul edt Udrescu, Mihai edt Udrescu, Lucreția oth Kurunczi, Ludovic oth Bogdan, Paul oth Udrescu, Mihai oth |
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English |
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author2 |
Kurunczi, Ludovic Bogdan, Paul Udrescu, Mihai Udrescu, Lucreția Kurunczi, Ludovic Bogdan, Paul Udrescu, Mihai |
author_facet |
Kurunczi, Ludovic Bogdan, Paul Udrescu, Mihai Udrescu, Lucreția Kurunczi, Ludovic Bogdan, Paul Udrescu, Mihai |
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l u lu l k lk p b pb m u mu |
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HerausgeberIn HerausgeberIn HerausgeberIn Sonstige Sonstige Sonstige Sonstige |
title |
In Silico Strategies for Prospective Drug Repositionings |
spellingShingle |
In Silico Strategies for Prospective Drug Repositionings |
title_full |
In Silico Strategies for Prospective Drug Repositionings |
title_fullStr |
In Silico Strategies for Prospective Drug Repositionings |
title_full_unstemmed |
In Silico Strategies for Prospective Drug Repositionings |
title_auth |
In Silico Strategies for Prospective Drug Repositionings |
title_new |
In Silico Strategies for Prospective Drug Repositionings |
title_sort |
in silico strategies for prospective drug repositionings |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
physical |
1 electronic resource (288 p.) |
isbn |
3-0365-6133-1 3-0365-6134-X |
illustrated |
Not Illustrated |
work_keys_str_mv |
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(CKB)5470000001633477 (oapen)https://directory.doabooks.org/handle/20.500.12854/95899 (EXLCZ)995470000001633477 |
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In Silico Strategies for Prospective Drug Repositionings |
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