Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

• DOI: 10.1038/sdata.2016.95
• Publication Date: 2016-11-21T15:16:08Z
• Authors: Jan Wildenhain; Michaela Spitzer; Sonam Dolma; Nick Jarvik; Rachel White; Marcia Roy; Emma Griffiths; David S. Bellows; Gerard D. Wright; Mike Tyers
• Keywords: Benchmarking; Benchmark (surveying); Chemical similarity

The network structure of biological systems suggests that effective therapeutic intervention may require combinations agents act synergistically. However, a dearth systematic chemical combination datasets have limited the development predictive algorithms for synergism. Here, we report two large linked chemical-genetic and chemical-chemical interactions in budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse gene deletion strains to generate an extended matrix (CGM) 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. selected 128 structurally cryptagens tested all pairwise benchmark 8,128 tests synergy prediction, cryptagen (CM). An accompanying database resource called ChemGRID was developed enable analysis, visualisation downloads data. CM will facilitate benchmarking computational approaches as well structure-activity relationship models anti-fungal drug discovery.