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008			210803s2021 xxua o |||| 0|eng d
020			_a9781071613665
024	7		_a10.1007/978-1-0716-1366-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRM301.25 _b2021 EB
245	0	0	_aIn Silico Modeling of Drugs Against Coronaviruses : _bComputational Tools and Protocols _cedited by Kunal Roy
250			_a1st edition 2021
264		1	_aNew York, NY _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXVI, 788 páginas) _b191 ilustraciones, 162 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Pharmacology and Toxicology _x1940-6053
520			_aThis essential volume explores a variety of tools and protocols of structure-based (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network) and ligand-based (pharmacophore mapping, quantitative structure-activity relationships or QSARs) drug design for ranking and prioritization of candidate molecules in search of effective treatment strategy against coronaviruses. Beginning with an introductory section that discusses coronavirus interactions with humanity and COVID-19 in particular, the book then continues with sections on tools and methodologies, literature reports and case studies, as well as online tools and databases that can be used for computational anti-coronavirus drug research. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical detail and implementation advice that ensures high quality results in the lab. Comprehensive and timely, In Silico Modeling of Drugs Against Coronaviruses: Computational Tools and Protocols is an ideal reference for researchers working on the development of novel anti-coronavirus drugs for SARS-CoV-2 and for coronaviruses that will likely appear in the future.
988			_aSpringer_Protocols_2021
650		7	_2embne _9183058 _aFarmacología _xInvestigación
650		7	_2embne _9688508 _aInfecciones por coronavirus _xTratamiento
776	0	8	_iPrinted edition: _z9781071613658
776	0	8	_iPrinted edition: _z9781071613672
776	0	8	_iPrinted edition: _z9781071613689
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-1366-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b08/2023 _dz _eb _zSI