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001 978-3-319-56850-8
003 DE-He213
005 20200811140251.0
007 cr nn 008mamaa
008 170524s2017 gw | s |||| 0|eng d
020 _a9783319568508
_9978-3-319-56850-8
024 7 _a10.1007/978-3-319-56850-8
_2doi
050 4 _aQD450-801
072 7 _aPNRP
_2bicssc
072 7 _aSCI013050
_2bisacsh
072 7 _aPNRP
_2thema
082 0 4 _a541.2
_223
245 1 0 _aAdvances in QSAR Modeling
_h[electronic resource] :
_bApplications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences /
_cedited by Kunal Roy.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aX, 555 p. 132 illus., 71 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aChallenges and Advances in Computational Chemistry and Physics,
_x2542-4491 ;
_v24
505 0 _aPerformance parameters and validation practices in QSAR modeling -- Towards interpretable QSAR models -- The use of topological indices in QSAR and QSPR modeling -- The Maximum Common Substructure (MCS) search as a new tool for SAR and QSAR -- The universal approach for structural and physico-chemical interpretation of QSAR/QSPR models -- Generative Topographic Mapping approach -- Monte Carlo methods for solution of tasks in Environmental Sciences -- QSAR in Environmental Research -- QSAR applications for environmental chemical prioritization: Biotransformation of chemicals -- QSAR modeling in environmental risk assessment: application to the prediction of pesticide toxicity -- Counter propagation artificial neural network (CP ANN) models for prediction of carcinogenicity of non congeneric chemicals for regulatory uses -- Strategy for identification of critical nanomaterials properties linked to biological impacts: interlinking of experimental and computational approaches -- QSAR/QSPR modeling in the design of drug candidates with balanced pharmacodynamics and pharmacokinetic properties -- Molecular modeling of food chemicals as potential bioactive compounds -- On application QSARs in Food and Agricultural Sciences: History and Recent Developments.
520 _aThe book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool.   This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.
650 0 _aChemistry, Physical and theoretical.
650 0 _aPharmaceutical technology.
650 0 _aEnvironmental chemistry.
650 0 _aFood—Biotechnology.
650 0 _aMedicinal chemistry.
650 0 _aAgriculture.
650 1 4 _aTheoretical and Computational Chemistry.
_0https://scigraph.springernature.com/ontologies/product-market-codes/C25007
650 2 4 _aPharmaceutical Sciences/Technology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/B21010
650 2 4 _aEnvironmental Chemistry.
_0https://scigraph.springernature.com/ontologies/product-market-codes/U15000
650 2 4 _aFood Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/C15001
650 2 4 _aMedicinal Chemistry.
_0https://scigraph.springernature.com/ontologies/product-market-codes/C28000
650 2 4 _aAgriculture.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L11006
700 1 _aRoy, Kunal.
830 0 _aChallenges and Advances in Computational Chemistry and Physics,
_x2542-4491 ;
_v24
856 4 0 _uhttps://doi.org/10.1007/978-3-319-56850-8
912 _aZDB-2-CMS
912 _aZDB-2-SXC
942 _cEBK
999 _c202310
_d202310