Quantitative structure - property relationship models

Liu, R., So, S.-S. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility./. Chem. Inf. Comp. Set. 2001, 41, 1633-1639. 

Huanxiang L, Xiaojun Y, Ruisheng Zh, Mancang L, Zhide H, Botao F (2005) 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. J. Phys. Chem. Sect B. 109 20565-20571. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Harju M, Andersson, PL, Haglund P, et al. 2002. Multivariate physiochemical characterization and quantitative structure-property relationship modelling of polybrominated diphenyl ethers. Chemosphere 47 375-384. 

Katritzky, A.R. Fara, D.C. Yang, H. Karelson, M. Suzuki, T. Solov ev, V.P. Varnek, A. Quantitative structure-property relationship modeling of beta-cyclodextrin complexation free energies. J. Chem. Inf. Comp. Sci. 2004, 44 (2), 529-541. 

Corma, A Serra, J.M., Serna, P. and Moliner, M. (2005) Integrating high-throughput characterization into combinatorial heterogeneous catalysis unsupervised construction of quantitative structure/property relationship models. /. Catal., 232, 335. 

Ivanciuc, O., Ivanciuc, T., Cabrol-Bass, D. and Balaban, A.T. (2000b). Evaluation in Quantitative Structure-Property Relationship Models of Structural Descriptors Derived from Information-Theory Operators. J.Chem.Inf.Comput.Sci., 40,631-643. 

Distance-related indexes in the quantitative structure-property relationship modeling. 

Stanton, D.T. (2000) Development of a quantitative structure-property relationship model for estimating normal boiling points of small multifunctional organic molecules. /. Chem. 

Quantitative structure-property relationship models for the prediction of liquid heat capacity. QSAR Comb. Sci., 22, 29 8. 

Unlike regression, PLS is not based on the assumption of independent and precise X variables but rather on the more realistic assumption that X contains more or less collinear and noisy parameters. Partial least squares summarizes these X variables by means of a few orthogonal score vectors (taeT), and the matrix Y is also resumed in a few score vectors ( aeU) which are not orthogonal. Plots of columns from T and U provide a visual representation of the configuration of the observations in the X or Y space, respectively. The PLS procedure allows one to derive a number of factors and weights, which are used to describe the desired properties. Quantitative structure-property relationships models are built up from these factors and weights. 

B. Lucic, I. Lukovits, S. Nikolid, and N. Trinajstic, Distance-related indexes in the quantitative structure-property relationship modeling, J. Chem. Inf. Comput. Sci. 41 (2001) 527-535. B. Lucic, A. Milifievid, S. Nikolid, and N. Trinajstic, Harary index—Twelve years later, Croat. Chem. Acta 75 (2002) 847-868. 

T. Le, V.C. Epa, F.R. Burden, D.A. Winkler, Quantitative structure-property relationship modeling of diverse materials properties. Chem. Rev. 112, 2889-2919 (2012)... 

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