Background: Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. Methods: We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9 & NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4 & LPL). We then used Mendelian randomization to evaluate the effect of each score on coronary artery disease (CAD) risk, and to systematically estimate their effects on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Results: There was strong evidence of an effect of drug-based genetic scores on CAD risk with the exception of ANGPTL3. Genetically predicted effects on the blood metabolome were generally consistent amongst drug targets which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically-predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2=0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (e.g. HMGCR vs all 4 triglyceride targets had r2<0.02), despite largely consistent effects on risk of CAD. Furthermore, we highlight this discrepancy for specific metabolic traits, for example finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation (e.g. PCSK9: Beta=0.01, 95 CI%=-0.06 to 0.08, P=0.78). In contrast, all of the triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker (e.g. LPL: Beta=-0.43, 95 CI%=-0.37 to -0.48, P=9x10-50). Conclusions: Multiple lipid-modifying drug targets have therapeutically beneficial effects on CAD risk. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, with potential implications for biomarkers in clinical development and measuring treatment response.