Local indices for similarity analysis (LISA)-a 3D-QSAR formalism based on local molecular similarity

A simple quantitative structure activity relationship (QSAR) approach termed local indices for similarity analysis (LISA) has been developed. In this technique, the global molecular similarity is broken up as local similarity at each grid point surrounding the molecules and is used as a QSAR descriptor. In this way, a view of the molecular sites permitting favorable and rational changes to enhance activity is obtained. The local similarity index, calculated on the basis of Petke's formula, segregates the regions into "equivalent", "favored similar", and "disfavored similar" (alternatively "favored dissimilar") potentials with respect to a reference molecule in the data set. The method has been tested on three large and diverse data sets-thrombin, glycogen phosphorylase b, and thermolysin inhibitors. The QSAR models derived using genetic algorithm incorporated partial least square analysis statistics are found to be comparable to the ones obtained by the standard three-dimensional (3D)-QSAR methods, such as comparative molecular field analysis and comparative molecular similarity indices analysis. The graphical interpretation of the LISA models is straightforward, and the outcome of the models corroborates well with literature data. The LISA models give insight into the binding mechanisms of the ligand with the enzyme and allow fine-tuning of the molecules at the local level to improve their activity.