Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers. Prog Chem Org Nat Prod. 2019;110:177-238 Authors: Mayr F, Vieider C, Temml V, Stuppner H, Schuster D Abstract Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field. PMID: 31621014 [PubMed - in process]
Source: Progress in the chemistry of organic natural products - Category: Chemistry Tags: Prog Chem Org Nat Prod Source Type: research