LEADER 05590nam 2200709Ia 450 001 9910811555203321 005 20230801232604.0 010 $a3-527-64501-2 010 $a1-283-59696-2 010 $a9786613909411 010 $a3-527-64502-0 010 $a3-527-64512-8 035 $a(CKB)3460000000080887 035 $a(EBL)1021397 035 $a(OCoLC)818862492 035 $a(SSID)ssj0000700878 035 $a(PQKBManifestationID)11427608 035 $a(PQKBTitleCode)TC0000700878 035 $a(PQKBWorkID)10672676 035 $a(PQKB)10806093 035 $a(MiAaPQ)EBC1021397 035 $a(Au-PeEL)EBL1021397 035 $a(CaPaEBR)ebr10598733 035 $a(CaONFJC)MIL390941 035 $a(EXLCZ)993460000000080887 100 $a20111205d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStatistical modelling of molecular descriptors in QSAR/QSPR /$fedited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev 205 $a2nd ed. 210 $aWeinheim $cWiley-VCH ;$a[Chichester $cJohn Wiley, distributor]$dc2012 215 $a1 online resource (458 p.) 225 0 $aQuantitative and network biology ;$vv. 2 300 $aDescription based upon print version of record. 311 $a3-527-32434-8 320 $aIncludes bibliographical references and index. 327 $aStatistical Modelling of Molecular Descriptors in QSAR/QSPR; Contents; Preface; List of Contributors; 1 Current Modeling Methods Used in QSAR/QSPR; 1.1 Introduction; 1.2 Modeling Methods; 1.2.1 Methods for Regression Problems; 1.2.1.1 Multiple Linear Regression; 1.2.1.2 Partial Least Squares; 1.2.1.3 Feedforward Backpropagation Neural Network; 1.2.1.4 General Regression Neural Network; 1.2.1.5 Gaussian Processes; 1.2.2 Methods for Classification Problems; 1.2.2.1 Logistic Regression; 1.2.2.2 Linear Discriminant Analysis; 1.2.2.3 Decision Tree and Random Forest; 1.2.2.4 k-Nearest Neighbor 327 $a1.2.2.5 Probabilistic Neural Network1.2.2.6 Support Vector Machine; 1.3 Software for QSAR Development; 1.3.1 Structure Drawing or File Conversion; 1.3.2 3D Structure Generation; 1.3.3 Descriptor Calculation; 1.3.4 Modeling; 1.3.5 General purpose; 1.4 Conclusion; References; 2 Developing Best Practices for Descriptor-Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations; 2.1 Introduction; 2.1.1 Posing the Question; 2.1.2 Validating the Models; 2.1.3 Interpreting the Models; 2.2 Leveraging Experimental Data and Understanding their Limitations 327 $a2.3 Descriptors: The Lexicon of QSARs2.3.1 Classical QSAR Descriptors and Uses; 2.3.2 Experimentally Derived Descriptors; 2.3.2.1 Biodescriptors; 2.3.2.2 Descriptors from Spectroscopy/Spectrometry and Microscopy; 2.3.3 0D, 1D and 2D Computational Descriptors; 2.3.4 3D Descriptors and Beyond; 2.3.5 Local Molecular Surface Property Descriptors; 2.3.6 Quantum Chemical Descriptors; 2.4 Machine Learning Methods: The Grammar of QSARs; 2.4.1 Principal Component Analysis; 2.4.2 Factor Analysis 327 $a2.4.3 Multidimensional Scaling, Stochastic Proximity Embedding, and Other Nonlinear Dimensionality Reduction Methods2.4.4 Clustering; 2.4.5 Partial Least Squares (PLS); 2.4.6 k-Nearest Neighbors (kNN); 2.4.7 Neural Networks; 2.4.8 Ensemble Models; 2.4.9 Decision Trees and Random Forests; 2.4.10 Kernel Methods; 2.4.11 Ranking Methods; 2.5 Defining Modeling Strategies: Putting It All Together; 2.6 Conclusions; References; 3 Mold2 Molecular Descriptors for QSAR; 3.1 Background; 3.1.1 History of QSAR; 3.1.2 Introduction to QSAR; 3.1.3 Molecular Descriptors: Bridge for QSAR 327 $a3.1.3.1 Molecular Descriptors3.1.3.2 Role of Molecular Descriptors; 3.1.3.3 Types of Molecular Descriptors; 3.1.3.4 Calculation of Molecular Descriptors (Software Packages); 3.2 Mold2 Molecular Descriptors; 3.2.1 Description of Mold2 Descriptors; 3.2.1.1 Topological Descriptors; 3.2.1.2 Constitutional Descriptors; 3.2.1.3 Information Content-based Descriptors; 3.2.2 Calculation of Mold2 Descriptors; 3.2.3 Evaluation of Mold2 Descriptors; 3.2.3.1 Information Content by Shannon Entropy Analysis; 3.2.3.2 Correlations between Descriptors; 3.3 QSAR Using Mold2 Descriptors 327 $a3.3.1 Classification Models based on Mold2 Descriptors 330 $aThis handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR.The high-profile international author and editor team ensures excellent coverage of the topic, making th 410 0$aQuantitative and Network Biology (VCH) 606 $aBioinformatics 606 $aMolecules$xModels$xComputer simulation 615 0$aBioinformatics. 615 0$aMolecules$xModels$xComputer simulation. 676 $a572.80285 701 $aDehmer$b Matthias$f1968-$0860612 701 $aVarmuza$b Kurt$f1942-$01636172 701 $aBonchev$b Danail$020960 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811555203321 996 $aStatistical modelling of molecular descriptors in QSAR$93977324 997 $aUNINA