Virtual-Cardio-Drug: Ai-Powered Sbvs for Cardiovascular Drug Discovery

Cardiovascular disease (CVD), particularly heart failure (HF), remains a significant public health challenge in Europe, necessitating new therapeutic strategies. Current treatments have limited efficacy, especially for heart failure with preserved ejection fraction (HFpEF) and acute HF. Additionally, 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 computing 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 bioinformatics results into actionable therapeutic discoveries. This integrated approach significantly accelerates the identification of promising new therapies for heart failure.