AI-enhanced computational discovery of promising ALK5 inhibitors in a ultra-large chemical space library for cardiovascular Disease therapy

Cardiac fibrosis, characterized by excessive extracellular matrix deposition, is a critical contributor to cardiovascular diseases, including heart failure. Transforming growth factor-beta 1 signaling, especially through activin receptor-like kinase 5 (ALK5), plays a key role in cardiac fibroblast activation and fibrosis. Traditional drug discovery approaches face challenges in identifying ALK5 inhibitors. This study leverages computational methods to expedite the discovery of potential ALK5 inhibitors. An active learning model was trained to screen a vast compound library, resulting in the selection of promising candidates. Molecular fingerprint clustering analysis and the absorption, distribution, metabolism, excretion, toxicity evaluation further characterized these compounds. Machine learning-based quantitative structure - activity relationship models predicted their activity. Molecular dynamics simulations assessed binding stability in different environments. DE50349483 and DE21377883 emerged as promising candidates with potential inhibitory effects. This study showcases the power of computational methods in drug discovery, offering hope for innovative therapies in cardiac fibrosis.