Comparative QSAR model

Lateral validation of 3D-QSARs is a meta-analytical technique allowing the user to design molecules that would have specificity for one, but not other receptors, revealing differences and similarities between the targeted receptors or between the compared series as systems, not as individual compounds. Because of the underlying assumption that the compared QSAR models are correct, this technique cannot guarantee robustness or predictive and explanatory power. 

We will use this dataset later to demonstrate the kernel influence on the SVM regression, as well as the effect of modifying the tube radius e. However, we will not present QSAR statistics for the SVM model. Comparative QSAR models are shown in the section on SVM applications in chemistry. 

The variable selection methods have been also adopted for region selection in the area of 3D QSAR. For example, GOLPE [31] was developed with chemometric principles and q2-GRS [32] was developed based on independent CoMFA analyses of small areas of near-molecular space to address the issue of optimal region selection in CoMFA analysis. Both of these methods have been shown to improve the QSAR models compared to original CoMFA technique. 

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. 

This procedure assessed whether some of the different descriptors used by different equations were intercorrelated and, therefore, interchangeable [59]. The remaining diverse QSAR equations were further classified by size (number of descriptors they include). The best equations of each encountered size were kept for final validation with the VS molecules and for further analysis. Consensus models featuring average predictions over these equations were also generated and validated. We focus here on the discussion of the minimalist overlay-independent and overlay-based QSAR models, each including only six descriptors, and refer to the optimal consensus model of the overlay-based QSAR approach families for comparative purposes. 

Unfortunately, FfipFfop and HypoGen cannot process the large training sets of the size used for QSAR model building. The set of 29 most potent Cox2 inhibitors has been submitted to HipHop and the best of the resulting hypotheses has been qualitatively compared to the overlay-based QSAR model hypothesis. 

In order to estimate the true predictive power of a QSAR model, one needs to compare the predicted and observed activities of a sufficiently large external test set of compounds that were not used in the model development. One convenient parameter is an external defined as follows (similar to Eq. (1) for the training set) ... 

The published QSAR [59-61] and 3D-QSAR [62-65] models for HDAC inhibitors were used to explain the differences in activity of hydroxamate-based compounds. All the reported models, which showed moderate to good internal predictivity, were mainly used in a retrospectively way to explain the biological activities of H DAC inhibitors. Generally, the 3D-QSAR models were compared with ligand docking results to get insight into the structural requirements for anti-HDAC activity. 

Regardless of the methods used to create the descriptors and construct the equation for the models, there is a need to validate the model by comparing the predicted bioactivities with the Experimental Bioactivities. Using the data that created the model (an internal method) or using a separate data set (an external method) can help validate the QSAR model. To determine if the model can be... 

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