Cluster analysis of the DrugBank chemical space using molecular quantum numbers

DrugBank (>6000 approved and experimental drugs) was analyzed using molecular quantum numbers (MQNs), which are 42 integer value descriptors of molecular structure counting atoms, bonds, polar groups and topological features. Principal component analysis of MQN-space showed that drugs differ mostly by size (PC1, 67% variance) and structural rigidity and polarity (PC2, 18% variance). Twenty-eight groups of target specific drugs were recovered by proximity sorting in MQN-space as efficiently as by substructure fingerprint (SF) similarity, but in different order allowing for lead-hopping relationships not seen in SF similarity. Clustering by MQN- or SF-similarity produced very different types of clusters. Each of the 28 drug groups spread over different clusters in both MQN- and SF-clustering, and most clusters contained drugs from different target specific groups, showing that structure-based classifications only partially overlap with bioactivity. An MQN-browsable version of DrugBank is available at www.gdb.unibe.ch.