Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection

Abstract

An evaluation of a variety of structure-based clustering methods for use in compound selection is presented. The use of MACCS, Unity and Daylight 2D descriptors; Unity 3D rigid and flexible descriptors and two in-house 3D descriptors based on potential pharmacophore points, are considered. The use of Ward's and group-average hierarchical agglomerative, Guénoche hierarchical divisive, and Jarvis−Patrick nonhierarchical clustering methods are compared. The results suggest that 2D descriptors and hierarchical clustering methods are best at separating biologically active molecules from inactives, a prerequisite for a good compound selection method. In particular, the combination of MACCS descriptors and Ward's clustering was optimal.