Quantitative structure-activity relationships predictive models

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

For halogenated aromatic hydrocarbons like polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and polychlorinated dibenzo-p-dioxins (PCDDs) the binding to the aryl hydrocarbon (Ah) receptor regulates their toxicity [89]. The Ah receptor controls the induction of one of the cytochrome P450 enzymes in the liver. Toxic responses such as thymic atrophy, iveight loss, immu-notoxicity and acute lethality are associated ivith the relative affinity of PCBs, PCDFs and PCDDs for the Ah receptor [89]. The quantitative structure-activity relationship (QSAR) models predicting the affinity of the halogenated aromatic hydrocarbons ivith the Ah receptor describe the electron acceptor capability as well as the hydrophobicity and polarizability of the chemicals [89[. 

Clearly, molecular structure influences the reaction kinetics of organic compounds during their photocatalytic oxidation. This relationship between degradability and molecular structure may be described using quantitative structure-activity relationship (QSAR) models. QSAR models can be developed to predict kinetic rate constants for organic compounds with similar chemical structures. The following section discusses QSAR models developed by Tang and Hendrix (1998) as well as those developed by other researchers. 

W. Guba, G. Cruciani, Molecular Modeling and Prediction of Bioactivity, Proceedings of the European Symposium on Quantitative Structure-Activity Relationships Molecular Modeling and Prediction of Bioactivity, 12th, Copenhagen, Denmark, Aug. 23-28,1998. 

Another limitation and restriction of these models are the data reliability of the Henry s law constants. It is very important that accurate Henry s law constants shall be available for modeling an air stripper as all design parameters and costs are strongly sensitive to the Henry s law constants (22). For many common VOCs, the constants are available in books as well as the literature. For uncommon contaminants, the constants may be looked up in an extensive database by Sander (36) or predicted by using quantitative structure-activity relationship (QSAR) model for Henry s law constant (37). However, if the data are absent or data reliability is of question, pilot testing or laboratory measurement of the Henry s law constant is recommended (38). 

Singh AK. Development of quantitative structure-activity relationship (QSAR) models for predicting risk of exposure from carcinogens in animals. Cancer Invest 2001 19 611-20. 

In addition, traditional quantitative structure-activity relationship (QSAR) models were reported. Gozalbes et al. attempted to predict the blood-brain barrier permeabilities of four arylacetamides using linear discriminant analysis [65], while Medina-Franco et al. discriminated between active and inactive BCG compounds using two-dimensional (2D) and three-dimensional (3D) structural-similarity methods [66]. 

QSARs for Pand B. Since experimental data for persistence and bioaccumulation (as well as ecotoxicity) are often unavailable, quantitative structure activity relationship (QSAR) models are commonly used by Environment Canada, the US EPA, and other government agencies to predict values for these hazards. For the purposes of assigning levels of concern in the Green Screen for persistence and bioaccumulation, when measurable data are absent, QSARs are considered acceptable (for further discussion of the use and limits of QSARs to fill data gaps see section 4.4). 

In summary, the support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of nonpeptide HIV-1 protease inhibitors. Cenetic algorithm (CA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R2) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q ) on SVM model was 0.9672, which proves the reliability of this model. Omar Deeb is thankful for Al-Quds University for financial support. 

Prediction of ILs toxicity by quantitative structure-activity relationship (QSAR) modelling... 

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. 

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