Cluster significance analysis contrasted with three other quantitative structure-activity relationship methods

Enzyme classification using complex dynamic hemithioacetal systems

A complex dynamic hemithioacetal system was used in combination with pattern recognition methodology to classify lipases into distinct groups.

A complex dynamic hemithioacetal system was generated for the evaluation of lipase reactivities in organic media. In combination with pattern recognition methodology, twelve different lipases were successfully classified into four distinct groups following their reaction selectivities and reactivities. A probe lipase was further categorized using the training matrix with predicted reactivity.

A simple clustering technique to improve QSAR model selection and predictivity: application to a receptor independent 4D-QSAR analysis of cyclic urea derived inhibitors of HIV-1 protease

A training set of 50 tetrahydropyrimidine-2-one based inhibitors of HIV-1 protease, for which the -log K(i) values were measured, was used to construct receptor independent 4D-QSAR models. A novel clustering technique was employed to facilitate and improve model selection as well as test set predictions. Following the manifold model theory, five unique models were chosen by the clustering algorithm (q(2) = 0.81-0.84). The models were used to map the atom type morphology of the inhibitor binding site of HIV-1 protease as well as to predict the potencies (-log K(i)) of 10 test set compounds. The rank-difference correlation coefficient was used to evaluate the quality of the test set predictions, which was improved from 0.39 to 0.68 when the clustering technique was applied. The set of five models, collectively, identify the important binding characteristics of the HIV protease receptor site. This study demonstrates that the selected simple clustering technique provides a discrete algorithm for model selection, as well as improving the quality of test set, or unknown, compound prediction as determined by the rank-difference correlation coefficient.

Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family

Predictive models based on tuned molecular quantum similarity measures and their application to obtain quantitative structure-activity relationships (QSAR) are described. In the present paper, the corticosteroid-binding globulin binding affinity of a 31 steroid family is studied by means of a multilinear regression using molecular descriptors derived from mixed steric-electrostatic quantum similarity matrixes as parameters, obtaining satisfactory predictions. A systematic procedure to treat outliers by using triple-density quantum similarity measures is also presented. This method depicts an alternative to the grid-based QSAR techniques, providing a consistent approach that avoids problematic result dependency on the grid parameters.

The use of cluster significance analysis to identify asymmetric QSAR data sets in toxicology. An example with eye irritation data

Cluster significance analysis is a tool that allows the identification of "embedded clusters' in QSAR datasets. It is successfully applied to an eye irritation data set to show that these data are indeed asymmetric. The method identifies five parameters that form an embedded cluster of eye irritants amongst non irritants, although full separation is not achieved. This method has considerable potential to identify potential non-linearity in toxicology data sets and for parameter reduction. It is shown also that this can be obtained relatively quickly with an analysis performed on 100,000 subsets containing the same information as an analysis on 1,000,000 subsets.

Variable mapping of structure-activity relationships: application to 17-spirolactone derivatives with mineralocorticoid activity

Fifty-four steroid homologs, belonging to the series of 17-spirolactones, were modelled by molecular and quantum mechanics. We studied the affinity of these compounds for the cytosolic mineralocorticoid receptor by way of various parameters describing each structure and its molecular properties. After the failure of a classic preliminary QSAR study, demonstrating the nonlinear relationships between affinity and structural descriptors, we constructed a model allowing us to predict the affinity of new compounds. Our method is based on simple graphic tools coupled to a cluster significance analysis. A complementary study of the activity relating the prediction of the antagonist/agonist character of 37 high-affinity compounds was also carried out using the same methodology. The principal electronic and structural characteristics leading to a selective activity were revealed.

Quantitative structure-activity relationships of competitive inhibitors of phosphoenolpyruvate carboxylase

The quantitative structure-activity relationships (QSAR) of all known competitive inhibitors of the enzyme phosphoenolpyruvate carboxylase from C4 plants were investigated by means of molecular mechanics, the semiempirical quantum chemical methods MNDO and AM1, and the Hansch approach. In the case of phosphoenolpyruvate analogues, the hydrophobicity and steric impediment of the combined cis and trans substituents, the bond distance to the cis substituent along with its volume, dipole moment, the distance between the phosphorus and the carbonyl carbon, and the net electric charges on the phosphate and substituent groups are the main factors that govern their binding to the active site. For the phosphoglycolate analogues, the difference in the HOMO-LUMO energies, the magnitudes of their dipole moments and their non-polar surfaces, and the distance between the phosphorus and the carbonyl carbon are the variables that control their binding to the active site. These results, in conjunction with a discriminant analysis, also suggest that these inhibitors can actually be divided into two groups, according to the way they presumably interact with the active site.

Pattern recognition methods in rational drug design

Pattern recognition methods have much to offer the drug designer, particularly as the calculation and collation of data, both biological and physicochemical, becomes easier with the widespread use of computer databases, molecular modeling systems, and property prediction packages. Some of the techniques, however, suffer from difficulties in interpretation and the dangers of chance effects have received little attention. The wider use and understanding of these methods is expected to enhance their utility in drug design. Finally, it should be mentioned here that these methods are becoming applied increasingly in other areas of pharmaceutical research, e.g., the analysis of clinical data, and that new techniques for analysis continue to be developed and applied in this field.