LEADER 00921nam0-22003131i-450- 001 990003271800403321 005 20041103101531.0 035 $a000327180 035 $aFED01000327180 035 $a(Aleph)000327180FED01 035 $a000327180 100 $a20030910d1980----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aRelazione sullo stato di attuazione del programma pluriennale di attivitą e di spesa 1977$f80 210 $aTorino$cEda$d1980 215 $app. 381 610 0 $aPiemonte 676 $a074.014 710 02$aPiemonte$c$06390 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003271800403321 952 $a074.014.PIE.03$b2590$fDECGE 952 $aD 208$bs.i.$fDSS 959 $aDSS 959 $aDECGE 996 $aRelazione sullo stato di attuazione del programma pluriennale di attivitą e di spesa 1977$9450678 997 $aUNINA LEADER 05002nam 22007335 450 001 9910254144703321 005 20200701003808.0 010 $a3-319-56850-7 024 7 $a10.1007/978-3-319-56850-8 035 $a(CKB)3710000001388660 035 $a(DE-He213)978-3-319-56850-8 035 $a(MiAaPQ)EBC4865036 035 $z(PPN)25885622X 035 $a(PPN)201472384 035 $a(EXLCZ)993710000001388660 100 $a20170524d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in QSAR Modeling $eApplications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences /$fedited by Kunal Roy 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (X, 555 p. 132 illus., 71 illus. in color.) 225 1 $aChallenges and Advances in Computational Chemistry and Physics,$x2542-4491 ;$v24 311 $a3-319-56849-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $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. 330 $aThe book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included ? two areas which have only recently been able to exploit this versatile tool.   This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR. 410 0$aChallenges and Advances in Computational Chemistry and Physics,$x2542-4491 ;$v24 606 $aChemistry, Physical and theoretical 606 $aPharmaceutical technology 606 $aEnvironmental chemistry 606 $aFood?Biotechnology 606 $aPharmaceutical chemistry 606 $aAgriculture 606 $aTheoretical and Computational Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C25007 606 $aPharmaceutical Sciences/Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/B21010 606 $aEnvironmental Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/U15000 606 $aFood Science$3https://scigraph.springernature.com/ontologies/product-market-codes/C15001 606 $aMedicinal Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C28000 606 $aAgriculture$3https://scigraph.springernature.com/ontologies/product-market-codes/L11006 615 0$aChemistry, Physical and theoretical. 615 0$aPharmaceutical technology. 615 0$aEnvironmental chemistry. 615 0$aFood?Biotechnology. 615 0$aPharmaceutical chemistry. 615 0$aAgriculture. 615 14$aTheoretical and Computational Chemistry. 615 24$aPharmaceutical Sciences/Technology. 615 24$aEnvironmental Chemistry. 615 24$aFood Science. 615 24$aMedicinal Chemistry. 615 24$aAgriculture. 676 $a615.19 702 $aRoy$b Kunal$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254144703321 996 $aAdvances in QSAR Modeling$91560504 997 $aUNINA