Inhibition of the human Ether-a-go-go Related Gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naïve Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naïve Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation procedure and 85% accuracy for the test set of 120 molecules. For the two additional test sets, the model achieved 89.4% accuracy for the WOMBAT-PK test set, and 86.1% accuracy for the PubChem test set. The naïve Bayesian classifiers gave better predictions than the PR classifiers. Moreover, the Bayesian classifier, employing molecular fingerprints, highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage, which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.

The blockage of the voltage dependent ion channel encoded by human ether-a-go-go related gene (hERG) may lead to drug-induced QT interval prolongation, which is a critical side-effect of non-cardiovasular therapeutic agents. Therefore, identification of potential hERG channel blockers at the early stage of drug discovery process will decrease the risk of cardiotoxicity-related attritions in the later and more expensive development stage. Computational approaches provide economic and efficient ways to evaluate the hERG liability for large-scale compound libraries. In this review, the structure of the hERG channel is briefly outlined first. Then, the latest developments in the computational predictions of hERG channel blockers and the theoretical studies on modeling hERG-blocker interactions are summarized. Finally, the challenges of developing reliable prediction models of hERG blockers, as well as the strategies for surmounting these challenges, are discussed.

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Abstract-Age patterns observed in meteorite groups reflect the different thermal or impact histories experienced by their parent bodies. To assess the number of ordinary chondrite (OC) parent bodies rare-gas data in the Schultz and Kruse (1989) data base were used to calculate U,Th-He gas-retention ages. Most H-and LL-chondrite ages are high; -81% are >2.2 Ga. In contrast, most L-chondrite ages are low; -69% are :52.2 Ga, and -35% are :50.9 Ga. The latter fraction is substantially lower than the value of 44% given by Heymann (1967). The difference is attributed to the preferential inclusion of shocked L chondrites in early studies. Broad age peaks in the Hand LL groups near 3.4 Ga probably reflect thermal loss during metamorphism, but in the H distribution there is a hint of minor outgassing "events" near I Ga. The LlLL chondrites have chemical properties intermediate between and unresolvable from Land LL chondrites. The high ages of most LlLL chondrites are evidence against these originating on the L parent body; the LlLL age distribution is consistent with an origin on the LL parent body or on an independent body.

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