Predicting cardiac drug safety before the clinical trial.
An AI-powered platform that combines machine learning with biophysical cardiac cell simulation to assess drug-induced cardiac risk. Faster, more accurate, and earlier in the drug development pipeline.
Predict hERG IC50 from molecular structure using gradient-boosted models trained on 9,500+ compounds from ChEMBL.
Run O'Hara-Rudy cardiac cell simulations with multi-channel ion current analysis in real time.
40-drug benchmark validated against the FDA-endorsed CiPA initiative. AUC score of 0.86 for TdP risk classification.
Model physiological variability across patient populations to understand drug response distributions.
Analyse drug effects across hERG, ICaL, INa, INaL, and IKs ion channels simultaneously.
Generate comprehensive PDF safety reports with interactive visualisations, risk assessments, and regulatory summaries.
Enter SMILES molecular structure or select from the 40-drug CiPA reference database.
ML predicts IC50 values. Cardiac simulation runs action potential and current traces.
Receive a TdP risk classification with confidence scores and downloadable PDF report.
Whether you’re screening a single compound or running a full pipeline, CardioSafe AI is ready.