ALIGNMENT OF MOLECULES BY THE MONTE-CARLO OPTIMIZATION OF MOLECULAR SIMILARITY INDEXES

3D-QSAR uses statistical techniques to correlate calculated structural properties with target properties like biological activity. The compa rison of calculated structural properties is dependent upon the relati ve orientations of molecules in a given data set. Typically molecules are aligned by performing an overlap of common structural units. This ''alignment rule'' is adequate for a data set, that is closely related structurally, but is far more difficult to apply to either a diverse data set or on the basis of some structural property other than shape, even for sterically similar molecules. In this work we describe a new algorithm for molecular alignment based upon optimization of molecula r similarity indices. We show that this Monte Carlo based algorithm is more effective and robust than other optimizers applied previously to the similarity based alignment problem. We show that QSARs derived us ing the alignments generated by our algorithm are superior to QSARs de rived using the more common alignment of fitting of common structural units. (C) 1997 by John Wiley & Sons, Inc.