3-DIMENSIONAL QUANTITATIVE SIMILARITY-ACTIVITY RELATIONSHIPS (3D QSIAR) FROM SEAL SIMILARITY-MATRICES

The program SEAL is suited to describe the electrostatic, steric, hydr ophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calcul ated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similari ty can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity ma trix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set , the test set predictivities (r(pred)(2) = 0.53-0.84) of different PL S models, based on a weighted sum of molecular properties, are superio r to published results of CoMFA and CoMSIA studies (r(pred)(2) = 0.31- 0.40), regardless of whether a common alignment or individual, pairwis e alignments of all molecules are used in the calculation of the simil arity matrices. Training and test set selections have a significant in fluence on the external predictivities of the models. Although the SEA L similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantita tive similarity-activity analyses (3D QSiAR) is proposed for approache s which correlate 3D structure-derived similarity matrices with biolog ical activities.