A drug similarity network for understanding drug mechanism of action.

Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action.