Mibefradil Hydrochloride

The withdrawal of numerous approved drugs in late development stages, or even from the market, due to safety concerns remains a major challenge, contributing to the high attrition rate in drug discovery and development. Among these concerns, cardiotoxicity is a critical toxicological issue, particularly in oncology, as drugs can induce heart damage by triggering pathological conditions such as arrhythmia, myocardial infarction, and myocardial hypertrophy. Here, we introduce CUPID (Cardiotox Understanding Platform for Intelligent Drug Discovery), an explainable artificial intelligence (XAI) framework designed to predict cardiotoxicity associated with ERG (ether-à-go-go-related gene) potassium, Na_v1.5 sodium, and Ca_v1.2 calcium ion channels. The framework was trained using three carefully curated interspecies experimental datasets from the latest ChEMBL database (release 34) and the CSFP (Core-Substituent Fingerprint), which encodes molecular fragments derived from the decomposition of drug-like small molecules. By leveraging these experimental datasets, highly accurate explainable machine learning models were developed, achieving approximately 80 % accuracy in 5-fold stratified cross-validation analyses. CUPID provides a comprehensive risk assessment of early cardiotoxicity and a key feature is its interpretability: predictions are annotated with clear applicability domain information, while chemical substructures linked to cardiotoxicity risks are highlighted using SHAP (SHapley Additive exPlanations) values. This enhances molecular understanding and facilitates the rational design of safer bioactive compounds. Last but not least, CUPID is freely accessible at https://prometheus.farmacia.uniba.it/cupid.