Phenotypic screens are increasingly utilized in drug discovery for multiple purposes such as lead and/or tool compound finding, and target discovery. Using potent and selective chemical tool compounds against well-defined targets in phenotypic screens can help elucidate biological processes modulating assay phenotypes. Unfortunately the identification of such tools from large heterogeneous bioactivity databases is nontrivial and there is repeated use of published unselective compounds as phenotypic tools. Here we describe a computational model, the compound-target tool score (TS), which is an evidence-based quantitative confidence metric that can be used to systematically rank tool compounds for targets. The identified selective and nonselective tool compounds have applications in phenotypic assays for target hypothesis validation as well as assay development.
Keywords: Bioactivity data integration; Chemical probe; Phenotypic screen; Selectivity; Target hypothesis validation; Tool compound.