Cardiovascular disease (CVD), particularly heart failure (HF), remains a significant public health challenge in Europe, necessitating new therapeutic strategies. Current treat- ments have limited efficacy, especially for heart failure with preserved ejection fraction (HFpEF) and acute HF. Addition- ally, cardiovascular events induced by cancer therapies further complicate treatment outcomes. To address these challenges, we developed a comprehensive structure-based virtual screening (SBVS) pipeline integrating artificial intelligence-driven protein structure prediction, extensive ligand databases, high-throughput ligand screening, and robust molecular dynamics simulations. Our computational framework leverages high-performance com- puting resources and state-of-the-art deep learning techniques to enhance the accuracy and efficiency of candidate identification and validation. Moreover, a user-friendly web application ensures broad accessibility, facilitating the translation of complex bioin- formatics results into actionable therapeutic discoveries. This integrated approach significantly accelerates the identification of promising new therapies for heart failure.