| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/bmcbi/LeeYKORBLSOCLN19 |
| P356 | DOI | 10.1186/S12859-019-2814-5 |
| P932 | PMC publication ID | 6538553 |
| P698 | PubMed publication ID | 31138104 |
| P50 | author | Dokyun Na | Q58805409 |
| P2093 | author name string | Byung Ho Lee | |
| Donghyun Lee | |||
| Kwang-Seok Oh | |||
| Hyang-Mi Lee | |||
| Dae-Seop Shin | |||
| Ki-Hyeong Rhee | |||
| Myung-Ae Bae | |||
| Myeong-Sang Yu | |||
| Hyithaek Ceong | |||
| Sayada Reemsha Kazmi | |||
| Seong Yun Oh | |||
| P2860 | cites work | Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel | Q40645984 |
| Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity | Q42290305 | ||
| Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety? | Q42361494 | ||
| Support vector machines classification of hERG liabilities based on atom types | Q45357092 | ||
| Predicting the potency of hERG K⁺ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models. | Q45961861 | ||
| Binding studies and GRIND/ALMOND-based 3D QSAR analysis of benzothiazine type K(ATP)-channel openers | Q46588422 | ||
| An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network. | Q51967889 | ||
| Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K(+) channel blockers | Q57005299 | ||
| Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities | Q58553697 | ||
| GRIND-based 3D-QSAR and CoMFA to investigate topics dominated by hydrophobic interactions: The case of hERG K+ channel blockers | Q60435095 | ||
| Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers | Q79416357 | ||
| SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules | Q28090714 | ||
| ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage | Q28730653 | ||
| Extended-connectivity fingerprints | Q29616639 | ||
| Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier | Q31034560 | ||
| Molecular biology of K(+) channels and their role in cardiac arrhythmias | Q31817227 | ||
| Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties | Q33383073 | ||
| hERG potassium channels and cardiac arrhythmia | Q34504394 | ||
| How can we improve our understanding of cardiovascular safety liabilities to develop safer medicines? | Q35036150 | ||
| Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools | Q35229954 | ||
| Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity | Q36095155 | ||
| In silico prediction of hERG inhibition | Q38447279 | ||
| hERG me out. | Q38453156 | ||
| Modeling of The hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM). | Q38600481 | ||
| A binary QSAR model for classification of hERG potassium channel blockers | Q40017431 | ||
| P433 | issue | Suppl 10 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | 250 | |
| P577 | publication date | 2019-05-29 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | Computational determination of hERG-related cardiotoxicity of drug candidates | |
| P478 | volume | 20 |
| Q112609285 | CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles | cites work | P2860 |
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