The development of QSAR/QSPR models is a quite complex process, as outlined in Figure S9. [Pg.749]

Xu YJ, Gao H (2003) Dimension related distance and its application in QSAR/QSPR model error estimation. QSAR Comb Sci 22 422—429 [Pg.93]

Index scale can be used as a measure of hydrophobicity of compounds for QSAR/QSPR modeling studies [Ishihama, Oda et al., 1996 Fatemi, 2003], [Pg.138]

Figure 1.14 General scheme of constructing linear QSAR/QSPR models based on fragment descriptors. |

The concept of the applicability domain concerns the predictive use of QSAR/QSPR models and, then, is closely related to the concept of model validation ( validation techniques). In other vords, the applicability domain is a concept related to the quality of the QSAR/QSPR model predictions and prevention of the potential misuse of model s results. A key component of the prediction quality is indeed to define when a QSAR/QSPR model is suitable to predict a property/activity of a new compound [Tropsha, Gramatica et al, 2003 Jaworska, Nikolova-Jeliazkova et al, 2004 Dimitrov, Dimitrova et al, 2005 Jaworska, Nikolova-Jeliazkova et al, 2005 Netzeva, Worth et al, 2005 Nikolova-Jeliazkova and Jaworska, 2005], [Pg.18]

Note that spectral moments of the distance matrix increase very quickly, thus requiring a proper scaling to be used in QSAR/QSPR modeling. [Pg.106]

Graph-invariants have been successfully applied in characterizing the structural similarity/dissimilarity of molecules and in QSAR / QSPR modelling. [Pg.197]

Benchmarking studies on various biological and physicochemical properties 307,312 QSAR/QSPR models for involving fragment descriptors [Pg.28]

Steric effects are among the most relevant in modeling physico-chemical properties and biological activities, thus playing a fundamental role in QSAR/QSPR modeling. [Pg.737]

It should be noted that some invariance properties such as invariance to atom numbering and rototranslations are mandatory for molecular descriptors used in QSAR/QSPR modeling in several cases, chemical invariance is required, particularly when dealing with a series of compounds with different substituents moreover, conformational invariance is closely dependent on the considered problem. [Pg.516]

In general, coefficients, roots, and derivatives of counting polynomials can be used for characterization of molecular graphs and as molecular descriptors in QSAR/QSPR modeling. [Pg.177]

Another relevant aspect in structure/response correlations is the ability to obtain information about molecular structure from QSAR/QSPR models. In particular, the term reversible decoding (or inverse QSAR) denotes any procedure capable to reconstruct the molecular structure or fragment starting from molecular descriptor values, that is, once molecular [Pg.749]

A model will yield reliable predictions when model assumptions are fulfilled and unreliable predictions when they are violated. In particular, for QSAR/QSPR models, based on statistical mining techniques, the training set and the model prediction space are the basis for the estimation of space where predictions are reliable. [Pg.18]

Quantitative Infonnation Analysis is the term proposed by Kier to denote structure/response correlations, where the word analysis is chosen to avoid any restriction to QSAR/QSPR models, but naturally includes similarity/diversity analysis as well as any explorative analysis or model which refers not only to relationships with the molecular structure. [Pg.420]

Well-known substituent descriptors are the substituent constants that are experimentally determined descriptors among them, electronic substituent constants, steric substituent descriptors, and lipophihcity substituent descriptors such as Hansch-Fujita hydrophobic constants are the most commonly used in QSAR/QSPR modeling. [Pg.753]

Some basic concepts and definitions of statistics, chemometrics, algebra, graph theory, similarity/diversity, which are fundamental tools in the development and application of molecular descriptors, are also presented in the Handbook in some detail. More attention has been paid to information content, multivariate correlation, model complexity, variable selection, and parameters for model quality estimation, as these are the characteristic components of modern QSAR/QSPR modelling. [Pg.680]

The first approach to applicability domain evaluation is the statistical analysis of the training set, trying to define the best conditions for interpolated prediction that is usually more reliable than extrapolation. Extrapolation is not a problem in principle, because extrapolated results from theoretically well-founded models can often be reliable. However, QSAR/QSPR models are usually based on empirical, and limited experimental evidence and/or are only locally valid therefore, extrapolation usually results in high uncertainty and not reliable predictions. [Pg.18]

See also in sourсe #XX -- [ Pg.133 , Pg.134 , Pg.135 , Pg.136 , Pg.137 , Pg.138 , Pg.139 , Pg.140 , Pg.141 , Pg.142 , Pg.143 , Pg.144 , Pg.145 , Pg.146 , Pg.147 , Pg.148 , Pg.149 , Pg.150 ]

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