Treating Chemical Diversity in QSAR Analysis: Modeling Diverse HIV-1 Integrase Inhibitors Using 4D Fingerprints.

A set of 213 compounds across 12 structurally diverse classes of HIV-1 integrase inhibitors was used to develop and evaluate a combined clustering and QSAR modeling methodology to construct significant, reliable, and robust models for structurally diverse data sets. The trial-descriptor pool for both clustering- and QSAR-model building consisted of 4D fingerprints and classic QSAR descriptors. Clustering was carried out using a combination of the partitioning around medoids method and divisive hierarchical clustering. QSAR models were constructed for members of each cluster by linear-regression fitting and model optimization using the genetic function approximation. The 12 structurally diverse classes of integrase inhbitors were partitioned into five clusters from which corresponding QSAR models, overwhelmingly composed of 4D fingerprint descriptors, were constructed. Analysis of the five QSAR models suggests that three models correspond to structurally diverse inhibitors that likely bind at a common site...