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 antica...
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| Format: | Online |
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| Jezik: | angleščina |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online dostop: | ONIX_20230105_9783036561349_128 |
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| _version_ | 1869517905935204352 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-95899 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-958992024-03-31T13:10:23Z In Silico Strategies for Prospective Drug Repositionings Udrescu, Lucreția Kurunczi, Ludovic Bogdan, Paul Udrescu, Mihai 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 thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries 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. 2023-01-05T12:37:41Z 2023-01-05T12:37:41Z 2022 book ONIX_20230105_9783036561349_128 9783036561349 9783036561332 https://directory.doabooks.org/handle/20.500.12854/95899 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6556 https://mdpi.com/books/pdfview/book/6556 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6133-2 10.3390/books978-3-0365-6133-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036561349 9783036561332 288 Basel open access |
| spellingShingle | 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 thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries In Silico Strategies for Prospective Drug Repositionings |
| title | 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_short | In Silico Strategies for Prospective Drug Repositionings |
| title_sort | in silico strategies for prospective drug repositionings |
| topic | 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 thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries |
| topic_facet | 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 thema EDItEUR::M Medicine and Nursing thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KND Manufacturing industries |
| url | ONIX_20230105_9783036561349_128 |