QT/TdP Risk Screen


QT/TdP Risk Screen

An innovative screening tool that predicts a compound’s proarrhythmic risk using electrophysiology and machine learning


Clinically Validated

Validated on a set of 109 known drug compounds, 51 of which torsadogenic


Display and Export

Compounds lists can be uploaded and results can be downloaded in JSON or CSV format


Easy to Use

Well-designed wizard that guides through the setup of computation step by step

QT/TdP Risk Screen is based on electrophysiology simulations that estimate a compound effect on the action potential of isolated endocardial cells (o’Hara et al. 2011). After computing novel torsadogenic indices, a machine learning algorithm classifies the drug at the concentration of interest as safe or unsafe in line with the CredibleMeds classification criteria.

This tool is easy to use since it only requires the compound’s in vitro IC50 values towards the IKs, IKr, ICaL and INaL ion channels and the compound’s concentration values for which the assessment of the proarrhythmic potential risk is needed. 

The predictive performance of QT/TdP Risk Screen was evaluated by means of classifying the proarrhythmic risk of 109 known drug compounds, 51 of which torsadogenic (Llopis et al. 2020).

Research team

QT/TdP Risk Screen is the result of a collaboration among Universitat Politècnica de València (UPV, Valencia), Fundació Institut Hospital del Mar D’Investigacions Mèdiques (FIMIM, Barcelona), and InSilicoTrials Technologies

Articles & publications

Jordi Llopis, Julio Gomis-Tena, Jordi Cano, Lucia Romero, Javier Saiz, and Beatriz Trenor

Journal of Chemical Information and Modeling 2020 https://doi.org/10.1021/acs.jcim.0c00201

Lucia Romero, Jordi Cano, Julio Gomis-Tena, Beatriz Trenor, Ferran Sanz, Manuel Pastor, and Javier Saiz

Journal of Chemical Information and Modeling 58 (4), 867-878, 2018

Thomas O’Hara, Lászlo ́Virág, Andraás Varró, and Yoram Rudy

PLoS Computational Biology 7(5): e1002061, 2011

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