Linear QSAR models

On a quantitative basis, an accurate correlation analysis was carried out and some significant linear QSAR models were obtained, based on ad hoc defined size-shape descriptors. One of the best linear correlations based on the Vin index indicated that the higher the volume shared by a generic antagonist and the supermolecule (obtained by superimposing abanoquil and WB4101, Scheme 8.1), the higher is... 

Based on the EPI generated values for solubility (log Sol), octanol-water partitioning (log Kow), vapour pressure (log VP) and Henry s Law constants (log HLC) new linear QSAR models are build by estimating the relationships between the EPI generated data and available experimental data for up to 65 organophosphor insecticides, the general formula for the descriptors, Du to be used being... 

Linear QSAR Models for Narcosis Baseline Toxicity... 

To describe the reduced bioconcentration of superlipophilic compounds, non-linear QSAR models have been derived (Table 4.15). 

TABLE 3.41 Multi-Linear QSAR Models for the Trial Molecular Activities With the Full Molecular (M) Physicochemical Parameters of Table 3.36 and the Corresponding Activities of the Structural Alerts (ASA) From Table 3.40 (Putz et al., 2011c)... 

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]. 

Note that the lipophilicity parameter log P is defined as a decimal logarithm. The parabolic equation is only non-linear in the variable log P, but is linear in the coefficients. Hence, it can be solved by multiple linear regression (see Section 10.8). The bilinear equation, however, is non-linear in both the variable P and the coefficients, and can only be solved by means of non-linear regression techniques (see Chapter 11). It is approximately linear with a positive slope (/ ,) for small values of log P, while it is also approximately linear with a negative slope b + b for large values of log P. The term bilinear is used in this context to indicate that the QSAR model can be resolved into two linear relations for small and for large values of P, respectively. This definition differs from the one which has been introduced in the context of principal components analysis in Chapter 17. 

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

Cabrera et al. [50] modeled a set of 163 drugs using TOPS-MODE descriptors with a linear discriminant model to predict p-glycoprotein efflux. Model accuracy was 81% for the training set and 77.5% for a validation set of 40 molecules. A "combinatorial QSAR" approach was used by de Lima et al. [51] to test multiple model types (kNN, decision tree, binary QSAR, SVM) with multiple descriptor sets from various software packages (MolconnZ, Atom Pair, VoSurf, MOE) for the prediction of p-glycoprotein substrates for a dataset of 192 molecules. Best overall performance on a test set of 51 molecules was achieved with an SVM and AP or VolSurf descriptors (81% accuracy each). 

Compared with the artificial neural network (ANN) approach used in previous work to predict CN12 the linear regression model by QSAR is as good or better and easier to implement. The predicted CN values, some of which are tabulated in Table 1, will be employed below to evaluate the different catalytic strategies to optimize the fuel. 

The relevance of size-related properties of hERG-blocking molecules was also detected in a 2D QSAR model developed by Coi et al. [22] after the analysis of 82 compounds through the CODESSA method. These authors developed two multiparameter models with strong predictive properties, from which, besides the involvement of hydrophobic features, the importance of linearity as opposed to globularity of the hERG blockers emerged. 

Some improvement in the statistics of these bilinear regression models was achieved with respect to previous linear equations. Moreover, the possibility to generate in a fast way a large pool of QSAR models able to describe a biological... 

For QSAR model building purposes, the Cox2 inhibitor set was split into a learning set (LS, 80% of compounds) and a Validation set (VS, 20%). Splitting was done so as to ensure an equivalent relative distribution of actives and inactives throughout both sets, and ignoring the original provenience of the compounds. These sets were used to train and validate two types of linear QSAR approaches ... 

Each of these two QSAR model searches led to pools of several thousands of statistically valid linear equations, expressing the estimate of the Cox2 pICso value as linear combinations of molecular descriptors selected by a Genetic Algorithm (GA) [57,... 

Tab. 5.1 The six-variable linear overlay-independent Cox2 QSAR model ...

In the last several years, a set of BBB QSAR models have been developed. One of the top models, developed by Abraham et al. in 2006 [44], reached the predictive limit obtainable from the data set they used. The experimental errors of the logBB measurements were estimated to be 0.3. Their model utilized linear free energy relationship (LFER) as descriptors. For the 328-molecule data set, r2 and RMSE of the MLR model were 0.75 and 0.3 log units, respectively. Interestingly, the RMSE for their test set (n = 164) was even lower (0.25 log units). 

Data from HTS often has a relatively high error or noise content and provides a very low precision activity measure, often binary ( pass-fail ). This effectively makes linear activity modeling impossible and classification-based QSAR methods must be employed. The database of 455 compounds, each active against one... 

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