Pii: S1359-6446(97)01079-9

Q uantitative structure-activity relationships (QSAR) correlate, within congeneric series of compounds, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological activities, either with certain structural features (Free Wilson analysis) or with atomic, group or molecular properties, such as lipophilicity, polarizability, electronic and steric properties (Han;sch analysis) 1-13. Although relationships between lipophilicity and unspecific biological properties, such as narcotic, bactericidal, fungicidal, hemolytic and toxic properties, have been known since the turn of our century, the independent publications of the Free Wilson method 14 and of Hansch analysis 15, both in 1964, mark a milestone in the development of QSAR. Since then, QSAR equations have been used to describe thousands of biological activities within different series of drugs and drug candidates. Especially enzyme inhibition data have been successfully correlated with physicochemical properties of the ligands 1-3,16. In cen:ain cases, where X-ray structures of the proteins became available, the results of QSAR regression models could be interpreted with the additional information from the three-climensional (31)) structures~-3. After a very slow development of grid-based 3D QSAR approaches, in 1988 the method of comparative molecular field analysis (CoMFA) was published by Cramer et al. 17 This molecular field-based method constituted the first real 3D QSAR method. In the past ten years, many successful CoMFA applications proved the value of this method 1~--'4, especially in cases where classical QSAR methods fail. In contrast to Hansch or Free Wilson analysis, CoMFA is better suited to describe ligand-receptor interactions, because it considers the properties of the ligands in their (supposed) bioactive conformations. As the result of a CoMFA analysis, regions in space are identified that are favorable or unfavorable for the ligand-receptor interaction.