Structure-based qspr modeling

A from the center of a positive ionizable group was identified. However, its predictive performance on a test set consisted of eight structurally similar compounds was relatively poor. To achieve a computational model with greater predictability, a descriptor-based QSPR model was also developed. Descriptors related to molecular hydrophobicity as well as hydrogen bond donor, shape and charge features contributed to explain hOCTl inhibitor properties of the analyzed compounds. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

Votano, J.R., Parham, M., Hall, L.H., Kier, L.B., Hall, L.M. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation. Chem. Biodivers. 2004, 1, 1829-41. 

Recently, Riviere and Brooks (2007) published a method to improve the prediction of dermal absorption of compounds dosed in complex chemical mixtures. The method predicts dermal absorption or penetration of topically applied compounds by developing quantitative structure-property relationship (QSPR) models based on linear free energy relations (LFERs). The QSPR equations are used to describe individual compound penetration based on the molecular descriptors for the compound, and these are modified by a mixture factor (MF), which accounts for the physical-chemical properties of the vehicle and mixture components. Principal components analysis is used to calculate the MF based on percentage composition of the vehicle and mixture components and physical-chemical properties. 

The physical analyses of the constants k and have not been investigated at this stage. Further, as QSPR models can predict relations between molecular structures and boiling points, it should be possible to extend these models to surface tension prediction based on the above relation. A general and semiempirical correlation between the alkane chain length and surface tension has been described." ... 

The predictive power, robustness, and reliability of the QSAR/QSPR models depend critically on the use of appropriate molecular descriptors. A myriad of descriptors, either empirical or those calculated on the basis of the molecular structure alone ( theoretical descriptors), have been developed both for the predictive and cognitive purposes [1,2]. Many of those descriptors are based directly on the results of quantum-mechanical calculations or can be derived from the electronic wave function or electrostatic field of the molecule. It is the purpose of the present chapter to give an overview of such molecular descriptors, together with some key applications. 

In the following paragraphs, some application examples will be presented, starting with a short introduction to COSMO-RS (Section 9.2), followed by solubility predictions in pure and mixed solvents (Section 9.3). A modification using several reference solubilities is shown in Section 9.4 whereas Section 9.5 is about quantitative structure-property relationship (QSPR) models of the melting point and the enthalpy of fusion. The final Sections 9.6 and 9.7 deal with COSMO-RS-based coformer selection for cocrystal screening and the related issue of solvent selection to avoid solvate formation. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Pollutants with high VP tend to concentrate more in the vapor phase as compared to soil or water. Therefore, VP is a key physicochemical property essential for the assessment of chemical distribution in the environment. This property is also used in the design of various chemical engineering processes [49]. Additionally, VP can be used for the estimation of other important physicochemical properties. For example, one can calculate Henry s law constant, soil sorption coefficient, and partition coefficient from VP and aqueous solubility. We were therefore interested to model this important physicochemical property using quantitative structure-property relationships (QSPRs) based on calculated molecular descriptors [27]. 

In many cases of practical interest, no theoretically based mathematical equations exist for the relationships between x and y we sometimes know but often only assume that relationships exist. Examples are for instance modeling of the boiling point or the toxicity of chemical compounds by variables derived from the chemical structure (molecular descriptors). Investigation of quantitative structure-property or structure-activity relationships (QSPR/QSAR) by this approach requires multivariate calibration methods. For such purely empirical models—often with many variables—the... 

It is to be noted that the QSPR/QSAR analysis of nanosubstances based on elucidation of molecular structure by the molecular graph is ambiguous due to a large number of atoms involved in these molecular systems. Under such circumstances the chiral vector can be used as elucidation of structure of the carbon nanotubes (Toropov et al., 2007c). The SMILES-like representation information for nanomaterials is also able to provide reasonable good predictive models (Toropov and Leszczynski, 2006a). 

Various methods by which the Kow of PAHs could be calculated are based on their molecular structures, i. e., a quantitative structure-property relationship (QSPR) approach [ 1,199,200]. One of the most famous techniques involves summation of hydrophobic fragmental constants (or f-values) for all groups in a molecule of a specific compound. On the other hand, Aboul-Kassim [1] and Aboul-Kassim et al. [202, 203] introduced a modeling technique based on the molecular connectivity indices of various PAHs, ranging from low- to high-molecular weight compounds. More details are given in Chap. 4 of this volume. 

The examples presented here help us to address the question of whether to use a model-based (small sets of simply interpreted descriptors) or nonmodel-based QSAR/QSPR method (large sets of descriptors). The model-based equations (which includes LFERs) can be used to fairly readily predict the result of changing molecular structure on a property. This is because these equations can often be easily interpreted from a chemical viewpoint. The nonmodel-based equations are frequently not so easily interpreted... 

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