Richard D Beger — Page 2

PubMedDate not supplied

Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra.

The prediction of Log P is usually accomplished using either substructure or whole-molecule approaches. However, these methods are complicated, and previous whole-molecule approaches have not been successful for the prediction of Log P in very complex molecules. The observed chemical shifts in nuclear magnetic resonance (NMR) spectroscopy are related to the electrostatics at the nucleus, which are influenced by solute-solvent interactions. The different solvation effects on a molecule by either water or methanol have a strong effect on the NMR chemical shift value. Therefore, the chemical shift values observed in an aqueous and organic solvent should correlate to Log P. This paper develops a rapid, objective model of Log P based on molar volume, hydrogen bonds, and differences in calculated 13C NMR chemical shifts for a diverse set of compounds. A partial least squares (PLS) model of Log P built on the sum of carbon chemical shift differences in water and methanol, molar volume, number of hydrogen bond donors and acceptors in 162 diverse compounds gave an r2 value of 0.88. The average r2 for 10 training models of Log P made from 90% of the data was 0.87+/-0.01. The average q2 for 10 leave-10%-out cross-validation test sets was 0.87+/-0.05.

Carbon Isotopes↗

PubMedDate not supplied

Models of steroid binding based on the minimum deviation of structurally assigned 13C NMR spectra analysis (MiDSASA).

This paper develops a quantitative k-nearest neighbors modeling technique. The technique is used to demonstrate that a compound's biological binding activity to a receptor can be calculated from the minimum of the square root of the sum of squared deviations (SSSD) of a structurally assigned chemical shift on a template between the unknown compound to be predicted and a set of known compounds with known activities. When building models of biological activity, nonlinear relationships are built into the input training data. If a model is developed by selecting only compounds with minimum structurally assigned chemical shift deviations from the unknown compound, some of the nonlinear relationships can be removed. The smaller the total chemical shift deviation between a compound with known activity and another compound with unknown activity, the more likely it will have similar biological, chemical, and physical properties. This means that a model can be produced without rigorous statistics or neural networks. This technique is similar to structure-activity relationship (SAR) modeling, but instead of relying on substructure fragments to produce a model, this new model is based on minimum chemical shift differences on those substructure fragments. We refer to this method as minimum deviation of structurally assigned spectra analysis (MiDSASA) modeling. Modeling by the minimum deviation concept can be applied to other chemoinformatic data analyses such as metabolite concentrations in metabolic pathways for metabolomics research. A MiDSASA template model for 30 steroids binding the corticosterone binding globulin based on the activity factors of the two nearest compounds had a correlation of 0.88. A MiDSASA template model for 50 steroids binding the aromatse enzyme based on the average activity of the four nearest compounds had a correlation of 0.71.

Aromatase↗