Synthetic biology and metabolic engineeringare used to develop new strategies for producing valuablecompounds ranging from therapeutics to biofuels in engi-neered microorganisms. When developing methods forhigh-titer production cells, toxicity is an important elementto consider. Indeed the production rate can be limited due totoxic intermediates or accumulation of byproducts of theheterologous biosynthetic pathway of interest. Conversely,highly toxic molecules are desired when designing antimi-crobials. Compound toxicity in bacteria plays a major role inmetabolic engineering as well as in the development of newantibacterial agents. Here, we screened a diversified chemicallibrary of 166 compounds for toxicity in Escherichia coli. Thedataset was built using a clustering algorithm maximizingthe chemical diversity in the library. The resulting assay datawas used to develop a toxicity predictor that we used toassess the toxicity of metabolites throughout the metabo-lome. This new tool for predicting toxicity can thus be used for fine-tuning heterologous expression and can be integrat-ed in a computational-framework for metabolic pathwaydesign.