Building predictive QSAR and QSPR models is a cost-effective way to estimate biological activities, physicochemical properties such as partition coefficients and solubility, and more complicated pharmaceutical endpoints such as metabolic stability and volume of distribution. It seems to be reasonable to assume that structurally similar molecules should behave similarly. That is, similar molecules should have similar biological activities and physicochemical properties. This is the (Q)SAR/(Q)SPR hypothesis. Qualitatively, both molecular interactions and molecular properties are determined by, and therefore are functions of, molecular structures. Or [Pg.40]

Topological Indices and Related Descriptors in QSAR and QSPR J. Devillers, A. T. [Pg.249]

Breneman, C., and M. Martinov. 1996. The Use of the Electrostatic Potential Fields in QSAR and QSPR. In Molecular Electrostatic Potentials Concepts and Applications, edited by J. S. Murray and K. D. Sen. Elsevier, Amsterdam. [Pg.77]

Devillers, J. and Balaban, A. T. (eds.) (1999) Topological indices and related descriptors in QSAR and QSPR. Gordon and Breach Science Publishers, Amsterdam, The Netherlands. [Pg.46]

Devillers, J., Balaban, A. T. Topological indices and Related Descriptors In QSAR and QSPR, Gordon Breach, Amsterdam, 1999. [Pg.405]

This section represents different case studies to explain how physical and chemical properties, QSAR and QSPR approaches, and multicomponent toxic effect models can be used to predict the mobility and bioavailability of organic pollutants at aqueous-solid phase interfaces. Such interdisciplinary approaches are applied here to two groups of toxic and carcinogenic compounds. [Pg.273]

J.L. Faulon et al., The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J. Chem. Inf. Comput. Sci. 43, 707-720 (2003) [Pg.215]

Ivanduc, O., Ivanduc, T. Matrices and structural descriptors computed from molecular graph distances. In Topological Indices and Related Descriptors in QSAR and QSPR, Devillers, J. Balahan, A. T. (eds.), Gordon Breach, Amsterdam, 1999, pp. 221-277. [Pg.106]

There is a long history of efforts to find simple and interpretable /i and fi functions for various activities and properties (29, 30). The quest for predictive QSAR models started with Hammett s pioneer work to correlate molecular structures with chemical reactivities (30-32). However, the widespread applications of modern predictive QSAR and QSPR actually started with the seminal work of Hansch and coworkers on pesticides (29, 33, 34) and the developments of various powerful analysis tools, such as PLS (partial least squares) and neural networks, for multivariate analysis have fueled these widespread applications. Nowadays, numerous publications on guidelines, workflows, and [Pg.40]

By dividing the problem this way, we translate it from an abstract problem in catalysis to one of relating one multi dimensional space to another. This is still an abstract problem, but the advantage is that we can now quantify the relationship between spaces B and C using QSAR and QSPR models. Note that space B contains molecular descriptor values, rather than structures. These values, however, are directly related to the structures (8). [Pg.263]

In a broader context, LFER and similar approaches are subsets of correlation analyses. Exner defines correlation analysis as a mathematical treatment starting from experimental data and seeking empirical relationships which can subsequently be interpreted theoretically. Although certainly not restricted to chemistry, correlation analysis has been developed extensively in physical organic chemistry. In addition to LFER, LSER, QSAR, and QSPR involve empirical models and, hence, fall in the category of correlation analysis. [Pg.217]

In the present work, we will use a relatively low level of theory to derive 32 weakly correlated molecular descriptors, each based on the subdivision and classification of the molecular surface area according to three fundamental properties contribution to ClogP, molar refractivity, and atomic partial charge. The resulting collection will be shown to have applicability in QSAR, QSPR, and compound classification. Moreover, the derived 32 descriptors linearly encode most of the information of a collection of traditional mathematical descriptors used in QSAR and QSPR. [Pg.262]

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships. [Pg.390]

From another viewpoint, LFER methods tend to be model based. Model-based methods employ sets of descriptors that often (1) model classical chemical concepts, (2) are small in number, and (3) use simple regression analyses. For example, the Flammett equation involving the logarithm of the rate constant as a linear function of the substituent constant, a (mentioned earlier), is model based. Similarly, some QSAR and QSPR studies may be viewed in this manner, and so they are included as LFER subsets in this chapter. [Pg.217]

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