QSPR based models

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]. 

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. 

Knowledge-based Modelling QSPR/QSAR Methods and Neural Networks. 

Lucic, B. and Trinajstic, N. (1997). New Developments in QSPR/QSAR Modeling Based on Topological Indices. SAR QSAR Environ.Res., 7,45-62. 

Clark, T. (2004) QSAR and QSPR based solely on surface properties J. Mol. Graph. Model., 22, 519-525. 

Duchowicz, P.R., Sinani, R.G., Castro, E.A. and Toropov, A.A. (2003) Maximum topological distances based indices as molecular descriptors for QSPR. V. Modeling the free energy of hydrocarbons. Indian J. Chem., 42, 1354—1359. 

Lucic, B. andTrinajstic, N. (1997) New developments in QSPR/QSAR modeling based on topological indices. SAR SI QSAR Environ. Res., 7, 45-62. 

Ren, B. (2002c) Novel atomic-level-based AI topological descriptors application to QSPR/QSAR modeling./. Chem. Inf. Comput. Sci., 42, 858-868. 

Briefly, the PSP approach heavily resides on the quantum mechanics-based COSMO-RS theory of solutions [17-22], The COSMO model belongs to the class of continuum solvation models (CSM) of quantum mechanics. For the solvation picture, it considers the molecule embedded in a conductor of infinite permittivity that screens perfectly the molecular charges on the surface of its molecular cavity. This molecular cavity is characterized by a volume, Fcogni, and a molecular surface area, The crucial information is contained in the so-called COSMO tile of each compound obtained from quantum chemical calculations at various levels of theory. COSMO tiles give the detailed surface charge distribution or the o-protile of each molecule. The o-protile may be analyzed into its moments of various orders, known as COSMOments, out of which a large number of properties may be calculated, among them the molecular descriptors of Abraham s QSPR/LSER model [23,24]. 

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. 

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). 

Recently, several QSPR solubility prediction models based on a fairly large and diverse data set were generated. Huuskonen developed the models using MLRA and back-propagation neural networks (BPG) on a data set of 1297 diverse compoimds [22]. The compounds were described by 24 atom-type E-state indices and six other topological indices. For the 413 compoimds in the test set, MLRA gave = 0.88 and s = 0.71 and neural network provided... 

We also note that many ADME, QSAR or QSPR models, based on experimental or computed parameters, use a combination of log P and partial charges and/or fraction ionized at a given pH, as independent variables, rather than the potentially more physiological log or log values. This tendency may reflect a perceived superiority and accuracy of the logP values, whether computed or experimentally determined, and may also be reflected by the nature of the data stored observed among different industrial settings. 

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. 

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). 

Statistical characteristics of QSPRs obtained by the SMILES-based descriptors in three runs of the Monte Carlo optimization are presented in Table 14.1. One can see from Table 14.1 that statistical quality of these models is reproduced in all the three mns of optimization... 

Toropov AA, Toropova AP, Gutman I (2005b) Comparison of QSPR models based on hydrogen-filled graphs and on graphs of atomic orbitals. Croat. Chem. Acta 78 503-509. 

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. 

Narayanan and Gunturi [33] developed QSPR models based on in vivo blood-brain permeation data of 88 diverse compounds, 324 descriptors, and a systematic variable selection method called Variable Selection and Modeling method based on the Prediction (VSMP). VSMP efficiently explored all... 

As a possible alternative to in vitro metabolism studies, QSAR and molecular modelling may play an increasing role. Quantitative stracture-pharmacokinetic relationships (QSPR) have been studied for nearly three decades [42,45-52]. These are often based on classical QSAR approaches based on multiple linear regression. In its most simple form, the relationship between PK properties and lipophilicity has been discussed by various workers in the field [36, 49, 50]. 

Liu and Zhong introduced a number of QSPR models based on molecular connectivity indices [151, 152], In a first iteration, the researchers developed polymer-dependent correlations descriptors were calculated for a set of solvents and models were developed per polymer type [151], Polymer classes under consideration were polystyrene, polyethylene, poly-1-butene, poly-l-pentene, poly(4-methyl-l-pentene), polydimethylsiloxane, and polyisobutylene. As the authors fail to provide any validation for their models, it is difficult to asses their predictive power. In a subsequent iteration and general expansion of this study, mixed and therefore more general models based on the calculated connectivity indices of both solvent and polymers were developed. While it is unclear from the paper which polymer representation was used for the calculation of the connectivity indices, the best regression model (eight parameter model) yields only acceptable predictive power (R = 0.77, = 0.77, s = 34.47 for the training set, R = 0.75... 

Liu W, Yi P, Tang Z (2006) QSPR Models for various proeprties of polymethacrylates based on quantum chemical descriptors. QSAR Comb Sci 25 936-943... 

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. 

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