QSPR studies

Quantum chemical descriptors such as atomic charges, HOMO and LUMO energies, HOMO and LUMO orbital energy differences, atom-atom polarizabilities, super-delocalizabilities, molecular polarizabilities, dipole moments, and energies sucb as the beat of formation, ionization potential, electron affinity, and energy of protonation are applicable in QSAR/QSPR studies. A review is given by Karelson et al. [45]. 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Figure 10.1-1. Flow chart for the general model building process in QSPR studies.

The first step in developing a QSPR equation is to compile a list of compounds for which the experimentally determined property is known. Ideally, this list should be very large. Often, thousands of compounds are used in a QSPR study. If there are fewer compounds on the list than parameters to be fitted in the equation, then the curve fit will fail. If the same number exists for both, then an exact fit will be obtained. This exact fit is misleading because it fits the equation to all the anomalies in the data, it does not necessarily reflect all the correct trends necessary for a predictive method. In order to ensure that the method will be predictive, there should ideally be 10 times as many test compounds as fitted parameters. The choice of compounds is also important. For... 

A Brief Review of the QSAR Technique. Most of the 2D QSAR methods employ graph theoretic indices to characterize molecular structures, which have been extensively studied by Radic, Kier, and Hall [see 23]. Although these structural indices represent different aspects of the molecular structures, their physicochemical meaning is unclear. The successful applications of these topological indices combined with MLR analysis have been summarized recently. Similarly, the ADAPT system employs topological indices as well as other structural parameters (e.g., steric and quantum mechanical parameters) coupled with MLR method for QSAR analysis [24]. It has been extensively applied to QSAR/QSPR studies in analytical chemistry, toxicity analysis, and other biological activity prediction. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least-squares (PLS) analysis has been employed [25]. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

With all QSPR studies it is not possible to separate the influence of the data used to train the model and the computational approach used to derive the model from the final model. Ideally, the QSPR should be sufficiently general to be applied to any compound that is reasonably represented by the data used to derive the model. 

Katritzky, A. R., Wang, Y., Sild, S., Tamm, T., QSPR studies on vapor pressure, aqueous solubility, and the prediction of water-air partition coefficients, J. Chem. Inf. Comput. Sci. 1998, 38, 720-725. 

Karelson, M. and Lobanov, V.S. (1996). Quantum-chemical descriptors in QSAR/ QSPR studies. Chemical Reviews 96 1027-1043. 

Thiadiazole 1 and its derivatives were used as model compounds for the calculation of molecular parameters related to physical properties for their use in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies <1999EJM41, 2003IJB2583, 2005JMT27>. 

Toropov AA, Leszczynska D, Leszczynski J (2007a) QSPR study on solubility of fullerene C60 in organic solvents using optimal descriptors calculated with SMILES Chem. Phys. Lett. 441 119-122. 

While both the Bicerano and van Krevelen systems model a significant number of polymer properties, most QSPR studies have focused on only a small number of key properties (which is mainly correlated to the availability of data for model development). 

There have been isolated QSPR studies of a number of other polymer properties. These include the dielectric constant [144], the dielectric dissipation factor (tan 8) [168], the solubility parameter [169], the molar thermal decomposition function [170], the vitrification temperature of polyarylene oxides [171], and quantities relating to molecularly imprinted polymers [172, 173]. The interested reader is referred to the literature for further information. 

Zhou, D.S., Alelyunas, Y., Liu, R.F. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility. J. Chem. Inf. Model. 2008, 48, 981-7. 

Computable molecular descriptors that occur most frequently in QSPRs in this book are explained in Chapter 2. QSPRs and their statistical parameters are presented in the same way as shown for QPPRs in Section 1.4. Often, QSPR studies apply a set of molecular descriptors to compare their significance for the particular correlation. In this book we present only the most significant QSPRs as judged in the source or by the authors. 

Varnek, A., Fourches, D., Solov ev, V.P. et al. 2004. In Silico design of new uranyl extractants based on phosphoryl-containing podands QSPR studies, generation and screening of virtual combinatorial library and experimental tests. J. Chem. Inf. Comput. Sci. 44 (4) 1365-1382. 

The goal of this work is to provide an overview of QSPR studies in metal complexation and extraction and to discuss under which conditions QSPR modeling may become a valuable tool for computer-aided design of new metal binders. Early empirical correlations will be analyzed here only for comparison with regular QSPRs. 

Generally, tens of different machine-learning techniques are used in QSAR/QSPR studies. In this chapter, we describe only those that have been recently applied in studies of solvent extraction and complexation of metals. 

In this section, two types of structure-metal binding ability relationships will be described. The first one concerns empirical linear correlations between equilibrium constants of complexation or extraction and some descriptors. In most cases, these correlations are obtained for relatively small datasets (less than 20 molecules) without any validation. We do not intend to analyze them in detail only their general characteristics will be reported. The second type of relationships were obtained in regular QSPR studies involving the selection of pertinent descriptors from their large initial pools, and the stage of the models, validation on external test set(s). 

There are very few publications devoted to regular QSPR studies in solvent extractions. 

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)... 

T. Petrova et al., Modeling of fullerene C60 solubility in organic solvents The use of quantum-chemical and topological descriptors in QSPR study. J. Nanopart. Res., 2009 (submitted)... 

The aims of this chapter are to illustrate to the reader (1) the calculation of some commonly used TIs and structural descriptors, (2) the current status of the interpretation of topological indices, and (3) to make some recommendations regarding the application of the structural descriptors in QSPR studies. 

Ferreira, M.M.C., Polycyclic aromatic hydrocarbons a QSPR study, Chemosphere, 44, 125-146, 2001. 

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. 

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