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Ligand-based in silico hERG models were generated for 2 644 compounds using linear discriminant analysis (LDA) and support vector machines (SVM). As a result, the dataset used for the model generation is the largest publicly available (see Supporting Information). Extended connectivity fingerprints (ECFPs) and functional class fingerprints (FCFPs) were used to describe chemical space. All models showed area under curve (AUC) values ranging from 0.89 to 0.94 in a fivefold cross-validation, indicating high model consistency. Models correctly predicted 80 % of an additional, external test set; Y-scrambling was also performed to rule out chance correlation. Additionally models based on patch clamp data and radioligand binding data were generated separately to analyze their predictive ability when compared to combined models. To experimentally validate the models, 50 of the predicted hERG blockers from the Chembridge database and ten of the predicted non-hERG blockers from an in-house compound library were selected for biological evaluation. Out of those 50 predicted hERG blockers, tested at a concentration of 10 microM, 18 compounds showed more than 50 % displacement of [(3)H]astemizole binding to cell membranes expressing the hERG channel. K(i) values of four of the selected binders were determined to be in the micromolar and high nanomolar range (K(i) (VH01)=2.0 microM, K(i) (VH06)=0.15 microM, K(i) (VH19)=1.1 microM and K(i) (VH47)=18 microM). Of these four compounds, VH01 and VH47 showed also a second, even higher affinity binding site with K(i) values of 7.4 nM and 36 nM, respectively. In the case of non-hERG blockers, all ten compounds tested were found to be inactive, showing less than 50 % displacement of [(3)H]astemizole binding at 10 microM. These experimentally validated models were then used to virtually screen commercial compound databases to evaluate whether they contain hERG blockers. 109 784 (23 %) of Chembridge, 133 175 (38 %) of Chemdiv, 111 737 (31 %) of Asinex and 11 116 (18 %) of the Maybridge database were predicted to be hERG blockers by at least two of the models, a prediction which could, for example, be used as a pre-filtering tool for compounds with potential hERG liabilities.

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Cancer is one of the major causes of worldwide human mortality. A wide range of cytotoxic drugs are available on the market, and several compounds are in different phases of clinical trials. Many studies suggest that these cytotoxic molecules are also associated with different types of adverse side effects; therefore researchers around the globe are involved in the development of more efficient and safer anticancer drugs. In recent years, quinazoline and its derivatives have been considered as a novel class of cancer chemotherapeutic agents that show promising activity against different tumors. The aim of this article is to comprehensively review and highlight the recent developments concerning the anticancer activity of quinazoline derivatives as well as offer perspectives on the development of novel quinazoline derivatives as anticancer agents in the near future.

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In this study we followed a new approach to analyze molecular substructures required for hERG channel blockade. We designed and synthesized 40 analogues of dofetilide (1), a potent hERG potassium channel blocker, and established structure-activity relationships (SAR) for their interaction with this important cardiotoxicity-related off-target. Structural modifications to dofetilide were made by diversifying the substituents on the phenyl rings and the protonated nitrogen and by varying the carbon chain length. The analogues were evaluated in a radioligand binding assay and SAR data were derived with the aim to specify structural features that give rise to hERG toxicity.

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