Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

Occurrence of early afterdepolarization under healthy or hypertrophic cardiomyopathy conditions in the human ventricular endocardial myocyte: In silico study using 109 torsadogenic or non-torsadogenic compounds

(2022) Bernard Christophe   TOXICOLOGY AND APPLIED PHARMACOLOGY

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk

(2022) Jordi Llopis-Lorente et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Prediction of arrhythmia susceptibility through mathematical modeling and machine learning

(2021) Meera Varshneya et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A deep learning algorithm to translate and classify cardiac electrophysiology

(2021) Parya Aghasafari et al.   eLife

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

(2021) Mary M. Maleckar et al.   Frontiers in Physiology

Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs

(2021) Yedam Yoo et al.   Frontiers in Physiology

Blinded In Silico Drug Trial Reveals the Minimum Set of Ion Channels for Torsades de Pointes Risk Assessment

(2020) Xin Zhou et al.   Frontiers in Pharmacology

In-Silico Classifiers for the Assessment of Drug Proarrhythmicity

(2020) Jordi Llopis Lorente et al.   Journal of Chemical Information and Modeling

Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers

(2020) Noorain Khalifa et al.   Future Medicinal Chemistry

Array programming with NumPy

(2020) Charles R. Harris et al.   NATURE

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

(2019) Chuipu Cai et al.   Journal of Chemical Information and Modeling

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk

(2018) Lucia Romero et al.   Journal of Chemical Information and Modeling

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative

(2018) Zhihua Li et al.   CLINICAL PHARMACOLOGY & THERAPEUTICS

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

(2017) Elisa Passini et al.   Frontiers in Physiology

Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms

(2016) M Cummins Lancaster et al.   CLINICAL PHARMACOLOGY & THERAPEUTICS

Evolution of strategies to improve preclinical cardiac safety testing

(2016) Gary Gintant et al.   NATURE REVIEWS DRUG DISCOVERY

Rechanneling the cardiac proarrhythmia safety paradigm: A meeting report from the Cardiac Safety Research Consortium

(2013) Philip T. Sager et al.   AMERICAN HEART JOURNAL

Dealing with Global Safety Issues

(2013) Norman Stockbridge et al.   DRUG SAFETY

Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge

(2013) Kylie A. Beattie et al.   JOURNAL OF PHARMACOLOGICAL AND TOXICOLOGICAL METHODS

Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk

(2011) Gary R. Mirams et al.   CARDIOVASCULAR RESEARCH

A Multiscale Simulation System for the Prediction of Drug-Induced Cardiotoxicity

(2011) Cristian Obiol-Pardo et al.   Journal of Chemical Information and Modeling

Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation

(2011) Thomas O'Hara et al.   PLoS Computational Biology