Abstract
Unintended block of cardiac ion channels, particularly hERG (K(v)11.1), remains a key concern in drug development as disruption of ion channel function can lead to deadly arrhythmia. To assess proarrhythmic risk, we investigated how drugs interact with hERG in its open and inactivated states and whether drug interactions with other cardiac channels like Na(v)1.5 and Ca(v)1.2 mitigate that risk. Using cryo-EM structures, we modeled open and inactivated conformations of these channels with Rosetta and AlphaFold. We then applied Site Identification by Ligand Competitive Saturation (SILCS), a physics-based pre-computed ensemble docking method, to predict drug binding affinities. SILCS leverages molecular simulation-generated free energy maps for high-throughput docking against hydrated lipid bilayer-embedded ion channel models. Bayesian machine learning was used to refine SILCS scoring using experimental IC(50) values from 69 known hERG blockers outperforming Schrödinger Glide, AutoDock Vina, and OpenEye FRED drug docking predictions. Computed drug binding affinities for hERG and Ca(v)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 (TdP) 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 prior to experimental testing.