Arrhythmia risk predictions from molecular simulations of cardiac ion channel-drug interactions

作者：Kyle C. Rouen, Kush Narang, Yanxiao Han, David Wang, Ensley Jang, Sophia Brunkow, Vladimir Yarov‐Yarovoy, Alexander D. MacKerell, Igor Vorobyov · 发表于：Biophysical Journal · 年份：2025 · DOI：10.1016/j.bpj.2025.12.015 · 被引用次数：2 · 研究领域：Cardiac electrophysiology and arrhythmias、Computational Drug Discovery Methods、Ion channel regulation and function

1.2 channels were used to train machine learning models that successfully classified around 300 drugs from the CredibleMeds database. Cationic nitrogen SILCS fragment free energy scores were found to be top physical properties that are predictive of drug-induced torsades de pointes arrhythmia risk. This approach, which relies on the predicted binding free energies and predicted physical properties of drugs rather than the chemical structure of the drugs themselves as features, could be extended to facilitate the design of new drugs where rapid assessment of arrhythmia risk can be performed before experimental testing.