QSPR modeling of bioconcentration factor of nonionic compounds using Gaussian processes and theoretical descriptors derived from electrostatic potentials on molecular surface.

[1]  P. Wolfe Convergence Conditions for Ascent Methods. II , 1969 .

[2]  C. Ruepert,et al.  Quantitative structure-activity relationships for polycyclic aromatic hydrocarbons: Correlation between molecular connectivity, physico-chemical properties, bioconcentration and toxicity in Daphnia pulex , 1984 .

[3]  J. Murray,et al.  Partition coefficients of nitroaromatics expressed in terms of their molecular surface areas and electrostatic potentials , 1993 .

[4]  J. Murray,et al.  Statistically-based interaction indices derived from molecular surface electrostatic potentials: a general interaction properties function (GIPF) , 1994 .

[5]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[6]  Geoffrey E. Hinton,et al.  Evaluation of Gaussian processes and other methods for non-linear regression , 1997 .

[7]  S. Tao,et al.  Prediction of fish bioconcentration factors of nonpolar organic pollutants based on molecular connectivity indices. , 1999, Chemosphere.

[8]  M. Karelson Molecular descriptors in QSAR/QSPR , 2000 .

[9]  S. Tao,et al.  Estimation of bioconcentration factors of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors. , 2000, Chemosphere.

[10]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[11]  Yi-Zeng Liang,et al.  Monte Carlo cross validation , 2001 .

[12]  J. Zou,et al.  Correlation between empirical solvent polarity scales and computed quantities derived from molecular surface electrostatic potentials , 2001 .

[13]  Alessandro Pedretti,et al.  VEGA: a versatile program to convert, handle and visualize molecular structure on Windows-based PCs. , 2002, Journal of molecular graphics & modelling.

[14]  A Quantitative Structure−Property Relationship Analysis of logP for Disubstituted Benzenes , 2002 .

[15]  Yi-Zeng Liang,et al.  Monte Carlo cross‐validation for selecting a model and estimating the prediction error in multivariate calibration , 2004 .

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Safiye Sag Erdem,et al.  QSPR Study on the Bioconcentration Factors of Nonionic Organic Compounds in Fish by Characteristic Root Index and Semiempirical Molecular Descriptors , 2004, J. Chem. Inf. Model..

[18]  曾敏,et al.  多氯联苯的定量结构-性质(活性)关系 , 2005 .

[19]  K. Roy,et al.  QSPR of the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices , 2006, SAR and QSAR in environmental research.

[20]  Zhide Hu,et al.  The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine. , 2006, Chemosphere.

[21]  Gábor Csányi,et al.  Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties , 2007, J. Chem. Inf. Model..

[22]  J. Dearden,et al.  Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors. , 2007, Chemosphere.

[23]  J. Zou,et al.  QSPR models for the physicochemical properties of halogenated methyl-phenyl ethers. , 2008, Journal of molecular graphics & modelling.

[24]  Peng Zhou,et al.  Gaussian process: an alternative approach for QSAM modeling of peptides , 2008, Amino Acids.

[25]  F. Tian,et al.  Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm‐Gaussian processes , 2008, Biopolymers.

[26]  John Manchester,et al.  SAMFA: Simplifying Molecular Description for 3D-QSAR , 2008, J. Chem. Inf. Model..

[27]  F. Tian,et al.  Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches. , 2009, Analytica chimica acta.

[28]  Xiu-hong Liu,et al.  Prediction of Ion Drift Times for a Proteome‐Wide Peptide Set Using Partial Least Squares Regression, Least‐Squares Support Vector Machine and Gaussian Process , 2009 .

[29]  F. Tian,et al.  Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. , 2009, Journal of chromatography. A.

[30]  Mohammad Goodarzi,et al.  QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds , 2010, Environmental health insights.