Atherosclerosis is a leading global cause of disease, disability, and death, imposing a significant burden on healthcare systems. Despite medical advancements, optimising therapy for atherosclerotic cardiovascular disease (ASCVD) remains challenging, with treatment efficacy varying widely among individuals. This study explores the potential of the Pharmagenic Enrichment Score (PES) to identify optimal responders to ASCVD treatments, including statins, PCSK9 inhibitors, cholesterol absorption inhibitors, fibrates, and apoAI mimetic peptides. We constructed a drug-target gene protein-protein interaction network using String V12.0 and annotated the coordinates using biomaRt v2.56.0 for Ensembl annotation. These annotations were then used to filter CVD summary statistics. PES scores were generated for various ASCVD targets and trained, tuned, and validated in the UK Biobank cohort at various significance levels to assess their association with LDL-c and lifetime ASCVD risk. The utility of these scores were then further investigated using causal modelling. Our findings revealed that all ASCVD PES were significantly associated with ASCVD traits even after adjustment for genome-wide polygenic risk. This research aims to enhance personalised medicine in ASCVD treatment and may uncover genetic factors influencing individual responses to ASCVD drugs in the population. By integrating genetic information, we aim to refine treatment guidelines and improve outcomes for ASCVD patients.