The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several drugs with different therapeutic indications and recognized as hERG blockers were recently withdrawn due to the risk of QT prolongation, arrhythmia and Torsade de Pointes. In silico techniques can provide a priori knowledge of hERG blockers, thus reducing the costs associated with screening assays. Significant progress has been made in structure-based and ligand-based drug design and a number of models have been developed to predict hERG blockage. Although approaches such as homology modeling in combination with docking and molecular dynamics bring us closer to understand the drug-channel interactions whereas QSAR and classification models provide a faster assessment and detection of hERG-related drug toxicity, limitation on the applicability domain of the current models and integration of data from diverse in vitro approaches are still issues to challenge. Therefore, this review will emphasize on current methods to predict hERG blockers and the need of consistent data to improve the quality and performance of the in silico models. Finally, integration of network-based analysis on drugs inducing potentially long-QT syndrome and arrhythmia will be discussed as a new perspective for a better understanding of the drug responses in systems chemical biology.