05029nam 2201213z- 450 991063998770332120231214133552.03-0365-6133-1(CKB)5470000001633477(oapen)https://directory.doabooks.org/handle/20.500.12854/95899(EXLCZ)99547000000163347720202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierIn Silico Strategies for Prospective Drug RepositioningsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (288 p.)3-0365-6134-X 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.MedicinebicsscPharmaceutical industriesbicsscCOVID-19drug repurposingtopological data analysispersistent Betti functionSARS-CoV-2network-based pharmacologycombination therapynucleoside GS-441524fluoxetinesynergyantidepressantnatural compoundsQSARmolecular dockingdrug repositioningUK BiobankvaccineLC-2/ad cell linedrug discoverydockingMM-GBSA calculationmolecular dynamicscytotoxicity assayGWASmultiple sclerosisoxidative stressrepurposingADME-Toxbioinformaticscomplex network analysismodularity clusteringATC codehidradenitis suppurativaacne inversatranscriptomeproteomecomorbid disorderbiomarkersignaling pathwaydruggable genedrug-repositioningMEK inhibitorMM/GBSAGlide dockingMD simulationMM/PBSAsingle-cell RNA sequencingpulmonary fibrosisbiological networksp38α MAPKallosteric inhibitorsin silico screeningcomputer-aided drug discoverynetwork analysispsychiatric disordersmedicationspsychiatrymental disorderstoxoplasmosisToxoplasma gondiiin vitro screeningdrug targetsdrug-disease interactiontarget-disease interactionDPP4 inhibitorslipid raftsMedicinePharmaceutical industriesUdrescu Lucrețiaedt1279107Kurunczi LudovicedtBogdan PauledtUdrescu MihaiedtUdrescu LucrețiaothKurunczi LudovicothBogdan PaulothUdrescu MihaiothBOOK9910639987703321In Silico Strategies for Prospective Drug Repositionings3014587UNINA