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For: Yang PC, DeMarco KR, Aghasafari P, Jeng MT, Dawson JRD, Bekker S, Noskov SY, Yarov-Yarovoy V, Vorobyov I, Clancy CE. A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm. Circ Res 2020;126:947-64. [PMID: 32091972 DOI: 10.1161/CIRCRESAHA.119.316404] [Cited by in Crossref: 29] [Cited by in F6Publishing: 17] [Article Influence: 14.5] [Reference Citation Analysis]
Number Citing Articles
1 Rogers AJ, Selvalingam A, Alhusseini MI, Krummen DE, Corrado C, Abuzaid F, Baykaner T, Meyer C, Clopton P, Giles W, Bailis P, Niederer S, Wang PJ, Rappel WJ, Zaharia M, Narayan SM. Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death. Circ Res 2021;128:172-84. [PMID: 33167779 DOI: 10.1161/CIRCRESAHA.120.317345] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
2 Lee KH, Fant AD, Guo J, Guan A, Jung J, Kudaibergenova M, Miranda WE, Ku T, Cao J, Wacker S, Duff HJ, Newman AH, Noskov SY, Shi L. Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach. J Chem Inf Model 2021;61:4266-79. [PMID: 34420294 DOI: 10.1021/acs.jcim.1c00856] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Lagoutte-Renosi J, Allemand F, Ramseyer C, Yesylevskyy S, Davani S. Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives. Drug Discov Today 2021:S1359-6446(21)00533-X. [PMID: 34863931 DOI: 10.1016/j.drudis.2021.11.026] [Reference Citation Analysis]
4 Chen L, He Y, Wang X, Ge J, Li H. Ventricular voltage-gated ion channels: Detection, characteristics, mechanisms, and drug safety evaluation. Clin Transl Med 2021;11:e530. [PMID: 34709746 DOI: 10.1002/ctm2.530] [Reference Citation Analysis]
5 Ni H, Fogli Iseppe A, Giles WR, Narayan SM, Zhang H, Edwards AG, Morotti S, Grandi E. Populations of in silico myocytes and tissues reveal synergy of multiatrial-predominant K+ -current block in atrial fibrillation. Br J Pharmacol 2020;177:4497-515. [PMID: 32667679 DOI: 10.1111/bph.15198] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
6 Kudaibergenova M, Guo J, Khan HM, Zahid F, Lees-Miller J, Noskov SY, Duff HJ. Allosteric Coupling Between Drug Binding and the Aromatic Cassette in the Pore Domain of the hERG1 Channel: Implications for a State-Dependent Blockade. Front Pharmacol 2020;11:914. [PMID: 32694995 DOI: 10.3389/fphar.2020.00914] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
7 Passini E, Zhou X, Trovato C, Britton OJ, Bueno-orovio A, Rodriguez B. The virtual assay software for human in silico drug trials to augment drug cardiac testing. Journal of Computational Science 2021;52:101202. [DOI: 10.1016/j.jocs.2020.101202] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Mousaei M, Kudaibergenova M, MacKerell AD Jr, Noskov S. Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations. J Chem Inf Model 2020;60:6489-501. [PMID: 33196188 DOI: 10.1021/acs.jcim.0c01065] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
9 Trayanova NA, Popescu DM, Shade JK. Machine Learning in Arrhythmia and Electrophysiology. Circ Res 2021;128:544-66. [PMID: 33600229 DOI: 10.1161/CIRCRESAHA.120.317872] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
10 Peirlinck M, Sahli Costabal F, Kuhl E. Sex Differences in Drug-Induced Arrhythmogenesis. Front Physiol 2021;12:708435. [PMID: 34489728 DOI: 10.3389/fphys.2021.708435] [Reference Citation Analysis]
11 Fogli Iseppe A, Ni H, Zhu S, Zhang X, Coppini R, Yang PC, Srivatsa U, Clancy CE, Edwards AG, Morotti S, Grandi E. Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning. Clin Pharmacol Ther 2021;110:380-91. [PMID: 33772748 DOI: 10.1002/cpt.2240] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Bystricky W, Maier C, Gintant G, Bergau D, Carter D. Identification of Drug-Induced Multichannel Block and Proarrhythmic Risk in Humans Using Continuous T Vector Velocity Effect Profiles Derived From Surface Electrocardiograms. Front Physiol 2020;11:567383. [PMID: 33071822 DOI: 10.3389/fphys.2020.567383] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Clerx M, Mirams GR, Rogers AJ, Narayan SM, Giles WR. Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia. Front Physiol 2021;12:651162. [PMID: 34122128 DOI: 10.3389/fphys.2021.651162] [Reference Citation Analysis]
14 Aghasafari P, Yang PC, Kernik DC, Sakamoto K, Kanda Y, Kurokawa J, Vorobyov I, Clancy CE. A deep learning algorithm to translate and classify cardiac electrophysiology. Elife 2021;10:e68335. [PMID: 34212860 DOI: 10.7554/eLife.68335] [Reference Citation Analysis]
15 Whittaker DG, Capel RA, Hendrix M, Chan XHS, Herring N, White NJ, Mirams GR, Burton RB. Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine. R Soc Open Sci 2021;8:210235. [PMID: 33996135 DOI: 10.1098/rsos.210235] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
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17 Maleckar MM, Myklebust L, Uv J, Florvaag PM, Strøm V, Glinge C, Jabbari R, Vejlstrup N, Engstrøm T, Ahtarovski K, Jespersen T, Tfelt-Hansen J, Naumova V, Arevalo H. Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients. Front Physiol 2021;12:745349. [PMID: 34819872 DOI: 10.3389/fphys.2021.745349] [Reference Citation Analysis]
18 Heijman J, Sutanto H, Crijns HJGM, Nattel S, Trayanova NA. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovasc Res 2021;117:1682-99. [PMID: 33890620 DOI: 10.1093/cvr/cvab138] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Campana C, Dariolli R, Boutjdir M, Sobie EA. Inflammation as a Risk Factor in Cardiotoxicity: An Important Consideration for Screening During Drug Development. Front Pharmacol 2021;12:598549. [PMID: 33953668 DOI: 10.3389/fphar.2021.598549] [Reference Citation Analysis]
20 Sánchez de la Nava AM, González Mansilla A, González-Torrecilla E, Ávila P, Datino T, Bermejo J, Arenal Á, Fernández-Avilés F, Atienza F. Personalized Evaluation of Atrial Complexity of Patients Undergoing Atrial Fibrillation Ablation: A Clinical Computational Study. Biology (Basel) 2021;10:838. [PMID: 34571716 DOI: 10.3390/biology10090838] [Reference Citation Analysis]
21 Rudy Y. In Silico Pipeline for Drug Cardiotoxicity Assessment. Circ Res 2020;126:965-7. [PMID: 32271685 DOI: 10.1161/CIRCRESAHA.120.316901] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
22 Varshneya M, Mei X, Sobie EA. Prediction of arrhythmia susceptibility through mathematical modeling and machine learning. Proc Natl Acad Sci U S A 2021;118:e2104019118. [PMID: 34493665 DOI: 10.1073/pnas.2104019118] [Reference Citation Analysis]
23 Cano J, Zorio E, Mazzanti A, Arnau MÁ, Trenor B, Priori SG, Saiz J, Romero L. Ranolazine as an Alternative Therapy to Flecainide for SCN5A V411M Long QT Syndrome Type 3 Patients. Front Pharmacol 2020;11:580481. [PMID: 33519442 DOI: 10.3389/fphar.2020.580481] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]