A Novel Approach Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) for Prediction of hERG Liability

• Published in: Chemical research in toxicology Vol. 20; no. 2; pp. 217 - 226
• Main Author: Leong, Max K
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.02.2007
• Subjects: Algorithms; Computational Biology - methods; Computer Simulation; Databases as Topic; Drug Design; Ether-A-Go-Go Potassium Channels - antagonists & inhibitors; Ether-A-Go-Go Potassium Channels - chemistry; Humans; Inhibitory Concentration 50; Linear Models; Neural Networks (Computer); Potassium Channel Blockers - chemistry; Potassium Channel Blockers - pharmacology; Quantitative Structure-Activity Relationship; Reproducibility of Results
• ISSN: 0893-228X; 1520-5010
• DOI: 10.1021/tx060230c

A novel approach by using a panel of plausible pharmacophore hypothesis candidates to constitute the pharmacophore ensemble (PhE) and subject them to regression by support vector machine (SVM) has been developed for predicting the liability of human ether-a-go-go-related gene (hERG). This PhE/SVM scheme takes into account the protein conformational flexibility while interacting with structurally diverse ligands, which is crucial yet often neglected by most of the analogue-based modeling methods. Thirty-nine molecules were carefully selected and cross-examined from the literature data for this study, of which 26 and 13 molecules were deliberately treated as the training set and the test set to generate the model and to validate the generated model, respectively. The final PhE/SVM model gave rise to an r 2 value of 0.97 for observed vs predicted pIC50 values for the training set, a q 2 value of 0.89 by the 10-fold cross-validation and an r 2 value of 0.94 for the test set. Thus, this PhE/SVM model provides a fast and accurate tool for predicting liability of hERG and can be utilized to guide medicinal chemistry to avoid molecules with an inhibition potential of this potassium channel.