The new ChemAxon ADMET plugin group builds on the power of machine learning methods on curated data sets to support the drug design and medicinal chemistry optimization with reliable models and predictions. The first available pre-trained model is targeting the elimination of cardiotoxicity risk during drug discovery by predicting hERG inhibition.
In the presentation below, we demonstrate the model development and assessment data on our hERG pAct regression model. We also discuss the requirements of modern applications interfacing with machine learning models that lead to higher detail prediction results, such as estimated error, applicability domain, and the visualization of similar training compounds.