Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

Abstract

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.

Use of13C NMR Chemical Shift as QSAR/QSPR Descriptor

“…A comparative QSAR study was also performed by Asikainen et al with respect to the RBA for a diverse set of 36 estrogens to ER using three “spectroscopic” QSAR methods: eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA). The statistical parameters of different QSDAR models and the number of principal components (PCs) used in their development are shown in Table .…”

“…The EEVA model is in the borderline, whereas the performances of the EVA and CoSA models with 1 H NMR chemical shifts are substandard. On the basis of this comparative QSAR study, the authors suggested that the CoSA method with 13 C NMR chemical shifts is an alternative to the conventional 3D-QSAR methods for rationalizing and predicting the estrogenic activities of molecules …”

Prediction of 1H NMR Coupling Constants with Associative Neural Networks Trained for Chemical Shifts

“…Identification of individual signals in NMR spectra of complex mixtures can be assisted by structure-based predictions. Accurate predictions of NMR spectra enable the calibration of spectra-property relationships and chemometric models, exclusively on the basis of simulated data, which can then be applied to experimental data. , …”

On the Use of 1H and 13C 1D NMR Spectra as QSPR Descriptors

“…1 H NMR and 13 C NMR spectra have been used in several QSAR and QSPR studies. − Three different methods have been used in those studies to include NMR descriptors, although other approaches can be considered too. Most used is the whole spectrum approach. ,− As explained in the previous paragraph, shift areas will then correlate with the modeled activity. Optionally, specific features of the spectrum can be selected, for example, a few areas where relevant information is found.…”