Charged partial surface area (CPSA) descriptors QSAR applications.

The charged partial surface area, or CPSA descriptors were originally designed for use in structure-physical relationship studies to capture information about the features of molecules responsible for polar intermolecular interactions. Since their development, they have found applications in a broad variety of both structure-property and structure-activity relationship studies. In the present work, the CPSA descriptors are examined in more detail, evaluating their characteristics with regard to conformational dependence, sources of partial atomic charges, utility of whole molecule and substructure varieties, and the inclusion or exclusion of explicit hydrogens. Additionally, an examination of the physical interpretation that can be derived from structure-activity relationships that incorporate the CPSA descriptors is made. Most recently, the CPSA descriptors have been found to be practically useful in the study of acute aquatic toxicity where they appear to provide an alternative to LUMO energy level measures for describing global and local electrophilicity in cases of non-covalent molecular interactions. A second example illustrates the ability of the CPSA descriptors to discriminate agonists and antagonists among compounds that bind strongly at the estrogen receptor. While measures of global and local nucleophilicity and interatomic distances are required to explain receptor binding, volumetric parameters, such as CPSAs, were found to be necessary to provide separation between reactivity patterns for agonists and antagonists, all having high binding affinity to estrogen receptor.

Development of a quantitative structure--property relationship model for estimating normal boiling points of small multifunctional organic molecules.

Computer-assisted quantitative structure-property relationship techniques are applied in the development of a robust and accurate model of normal boiling points (boiling at 760 mmHg) for a very diverse set of 268 small organic molecules. Most of the molecules included in this study contain two or more functional groups. The final model yields a tight fit to the training set data (R2 = 0.963), with a fit error of 6.5%. More importantly, the model is also shown to perform well in external prediction. The mean prediction error for boiling points for a 78-member external test set was 12.3 degrees C, or 8.3%. A detailed analysis of the small number of compounds that were either outliers or not well predicted illustrates areas for potential improvement of the methodology used.

Application of nearest-neighbor and cluster analyses in pharmaceutical lead discovery.

High throughput screening (HTS) programs based on diverse collections of compounds can rapidly identify leads for potential drug candidates. In cases where the compound collection is truly diverse, one may only identify a few compounds of interest. However, where a large number of hits are identified, it becomes necessary to examine the structures to determine the true number of compound classes involved so that follow-up studies may be conducted as efficiently as possible. In this case, cluster analysis is applied to determine the structural relationship among HTS hits. To efficiently expand around the region of the hit (or a class of hits) in chemical space, we have applied nearest neighbors analysis to select additional compounds from collections of a large number of commercial vendors, achieving an average hit rate in excess of 15%. Applying these techniques in a number of different cases, we obtained results that are useful for subsequent investigations of hits from HTS and other relevant molecular structures from the literature.