ABSTRACT
Biological effects are mediated by intermolecular interactions, for instance, through the binding of a ligand to a receptor, which triggers a signaling event in a signal transduction pathway. Three-dimensional (3D) structures of receptor-ligand complexes are of great value to rationalize pharmacological or toxicological effects of small molecule ligands. However, due to experimental constraints such as purity and homogeneity of the protein, crystallizability, solubility, size of the protein-ligand complex, etc., an X-ray or NMR-based structure determination is often not feasible. In those cases empirical models have to be developed that deduce biological effects
from the 2D or 3D molecular structures of small molecule ligands only, and as a result structure-activity relationships (SAR) are formulated. These SARs may either be qualitative [i.e., molecular features (substructures, functional groups) are associated with activity] or quantitative SARs QSARs (defined by correlating molecular structures with biological effects via mathematical equations). QSAR models require the translation of molecular structures into numerical scales, i.e., molecular descriptors. These descriptors are used by various linear [partial least squares (PLS) (1), etc.] or non-linear [neural networks (2)] regression algorithms to predict biological effects of molecules which have not been tested or not even synthesized.
