A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition.

We report on the structural comparison of the corporate collections of Johnson & Johnson Pharmaceutical Research & Development (JNJPRD) and 3-Dimensional Pharmaceuticals (3DP), performed in the context of the recent acquisition of 3DP by JNJPRD. The main objective of the study was to assess the druglikeness of the 3DP library and the extent to which it enriched the chemical diversity of the JNJPRD corporate collection. The two databases, at the time of acquisition, collectively contained more than 1.1 million compounds with a clearly defined structural description. The analysis was based on a clustering approach and aimed at providing an intuitive quantitative estimate and visual representation of this enrichment. A novel hierarchical clustering algorithm called divisive k-means was employed in combination with Kelley's cluster-level selection method to partition the combined data set into clusters, and the diversity contribution of each library was evaluated as a function of the relative occupancy of these clusters. Typical 3DP chemotypes enriching the diversity of the JNJPRD collection were catalogued and visualized using a modified maximum common substructure algorithm. The joint collection of JNJPRD and 3DP compounds was also compared to other databases of known medicinally active or druglike compounds. The potential of the methodology for the analysis of very large chemical databases is discussed.