Critical comparative analysis, validation and interpretation of SVM and PLS regression models in a QSAR study on HIV-1 protease inhibitors

[1]  Yuhai Wu,et al.  Statistical Learning Theory , 2021, Technometrics.

[2]  R. Teófilo,et al.  Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression , 2009 .

[3]  Rajarshi Guha,et al.  On the interpretation and interpretability of quantitative structure–activity relationship models , 2008, J. Comput. Aided Mol. Des..

[4]  M. Asadollahi-Baboli,et al.  QSAR Analysis of Platelet-derived Growth Inhibitors Using GA-ANN and Shuffling Crossvalidation , 2008 .

[5]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[6]  A. Wlodawer,et al.  Structural characterization of B and non-B subtypes of HIV-protease: insights into the natural susceptibility to drug resistance development. , 2007, Journal of molecular biology.

[7]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[8]  Antony Browne,et al.  Extraction of comprehensible logical rules from neural networks: application of TREPAN in bio and cheminformatics , 2005 .

[9]  Ruisheng Zhang,et al.  Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines , 2005, J. Comput. Aided Mol. Des..

[10]  Zhide Hu,et al.  Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. , 2005, The journal of physical chemistry. B.

[11]  Rajni Garg,et al.  From SAR to comparative QSAR: role of hydrophobicity in the design of 4-hydroxy-5,6-dihydropyran-2-ones HIV-1 protease inhibitors. , 2005, Bioorganic & medicinal chemistry.

[12]  Wencong Lu,et al.  Support vector regression based QSPR for the prediction of some physicochemical properties of alkyl benzenes , 2005 .

[13]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[14]  Knut Baumann,et al.  Validation tools for variable subset regression , 2004, J. Comput. Aided Mol. Des..

[15]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[16]  Paola Gramatica,et al.  Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. , 2003, Environmental health perspectives.

[17]  Beyond—bernhard Schölkopf,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[18]  Knut Baumann,et al.  Cross-validation as the objective function for variable-selection techniques , 2003 .

[19]  M. M. Ferreira,et al.  A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. II. Molecular graphics and modeling. , 2003, Journal of molecular graphics & modelling.

[20]  M. M. Ferreira,et al.  QSAR of Progestogens: Use of a Priori and Computed Molecular Descriptors and Molecular Graphics , 2003 .

[21]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[22]  Rudolf Kiralj,et al.  A priori molecular descriptors in QSAR: a case of HIV-1 protease inhibitors. I. The chemometric approach. , 2003, Journal of molecular graphics & modelling.

[23]  A. Belousov,et al.  A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .

[24]  Jinbo Bi,et al.  Prediction of Protein Retention Times in Anion-Exchange Chromatography Systems Using Support Vector Regression , 2002, J. Chem. Inf. Comput. Sci..

[25]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[26]  Márcia M. C. Ferreira,et al.  Quimiometria I: calibração multivariada, um tutorial , 1999 .

[27]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[28]  R. Tobias.  Chemometrics: A Practical Guide , 1998, Technometrics.

[29]  T. Halgren,et al.  A priori prediction of activity for HIV-1 protease inhibitors employing energy minimization in the active site. , 1995, Journal of medicinal chemistry.

[30]  Mik Wisniewski,et al.  Applied Regression Analysis: A Research Tool , 1990 .

[31]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[32]  David M. Miller,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[33]  Stephen R. Johnson,et al.  The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy) , 2008, J. Chem. Inf. Model..

[34]  M. Forina,et al.  Multivariate calibration. , 2007, Journal of chromatography. A.

[35]  Chonghun Han,et al.  Modeling and Optimization of the Condensing Steam Turbine Network of a Chemical Plant , 2006 .

[36]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[37]  Alex Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[38]  Han van de Waterbeemd,et al.  Chemometric methods in molecular design , 1995 .

[39]  Shuhan Shena,et al.  Monocular 3 D tracking of deformable surfaces using sequential second order cone programming , 1979 .

[40]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[41]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .