Cardiac allograft vasculopathy (CAV) is the primary cause of chronic rejection in heart transplant (HTx) patients, affecting 50% of recipients within 10 years. It manifests as a progressive luminal narrowing of the graft coronary arteries, ultimately leading to heart failure. Currently, there are no effective treatments for CAV, and early diagnosis remains challenging, due to a lack of understanding of the mechanisms underlying the pathology’s development. To date, animal (in vivo) studies have been instrumental in deepening our comprehension of CAV risk factors. However, they are associated with ethical concerns, high resource demand, and can offer limited investigation time points. Computational (in silico) models can overcome these drawbacks, by providing a digital platform for investigating the multiscale processes occurring post-HTx.