The non-grid technique for modeling 3D QSAR using self-organizing neural network (SOM) and PLS analysis: Application to steroids and colchicinoids

A novel method for modeling 3D QSAR has been developed. The method involves a multiple training of a series of self-organizing networks (SOM). The obt ained networks have been used for processing the data of one reference mole cule. A scheme for the analysis of such data with the PLS analysis has been proposed and tested using the steroids data with corticosteroid binding gl obulin (CSG) affinity. The predictivity of the CBG models measured with the SDEP parameter is among the best one reported. Although 3-D QSAR models fo r colchicinoid series is far less predictive, it allows for a discussion on the relative influence of the structural motifs of these compounds.