Biological descriptors, QSAR

Olah M, Bologa C, Opre TI (2004) An automated PLS search for biologically relevant QSAR descriptors. J. Comp-Aid. Mol. Des. 18 437 149. 

Key Words Biological activity molecular descriptor QSAR QSPR molecular surface area ... 

Because of fhe stereospecifity of biological effects, QSAR (quantitative structure-activity relationships) methods must be capable of taking into account atomic chiralities. Indeed, one of fhe most popular 3D-QSAR methods, CoMFA and other CoMFA-like methods take into account chirality by default, since fhe molecular fields of chiral isomers are different If compounds are highly flexible and no experimental structural information about fhe receptor-ligand complexes is available, CoMFA (and CoMFA-like) methods are not always applicable. Several shortcomings and problems have motivated researchers to consider improvements to these techniques. The first idea for improvement was to modify the conventional 2D descriptors to make them chirahty-sensitive [1]. 

By structure-activity relationship (SAR), most chemists and biologists mean the filling out of a table of compounds and their biological activity. QSAR is the quantitative rationalization of such a table. Commonly the biological activity is regressed onto a set of molecular descriptors. 

Sedykh, A., Zhu, H., Tang, H., Zhang, L., Rusyn, I., Richard, A., et al. The use of dose-response qHTS data as biological descriptors improves the prediction accuracy of QSAR models of acute rat toxicity. Environmental Health Perspect, In press. 

The correlations between chemical descriptors of molecular properties and biological activity, especially the activity of herbicides and/or plant growth regulators has been described (12). Several alternatives or improvements on the Hansch-Fujita QSAR system have been developed (13—15). 

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. 

Some biological data are, of course, reported in a categorical manner, for example, as simply toxic or nontoxic. It is valid to class correlations involving such data as QSARs, because although the biological data are not quantitative (or are at best semiquantitative), the descriptors are. 

The concept of property space is progressively being used to gain a deeper understanding of the dynamic behavior of a single compound in different media (as we illustrate below with acetylcholine, see Section 1.4.2) or bound to biological targets (the carnosine-carnosinase complex, see Section 1.4.3), but it can be used also with a set of compounds to derive fertile descriptors for dynamic QSAR analyses (4D QSAR, see Section 1.4.4). 

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