This documentation gives a short introduction to ChemAxon's hERG (hERG-beta) Predictor.
hERG potassium channels play an essential role in normal electrical activity of the heart, mediating the cardiac action potential of the heartbeat. Affecting their activity by xenobiotics can have life-threatening consequences. Accordingly, hERG is one of the most important off-targets of drug discovery.
Optimisation to reduce the risk of inhibiting the hERG channels during discovery projects requires computational prediction in the early design throughout the pre-synthesis phase. The hERG channel inhibition capacity of a drug is measured by its hERG activity (Act).
Experimentally, hERG activity is determined by different electrophysiological methods and measured as IC50 or Ki values. However, as with other physico-chemical properties (e.g. pKa), the negative logarithm of the measured activity (pAct) is used in the literature:
pAct = -log10(Act)
Besides quantitative hERG values it is also common to provide a two-class classification of compounds for hERG activity.
We built two hERG models, an activity model and a two-class (TOXIC or SAFE) classification model.
The activity model was built using multiple publicly available data sources, including the hERG Central DataBase, ChEMBL and various selected publications and patents. The current version of the activity hERG model encapsulates structure activity relationships (SARs) from around 2500 pAct data points.
We ran a test to validate the predictive capabilities of the activity model on a test set of around 270 molecules. The test resulted in a 0.80 Pearson correlation coefficient and a 0.55 RMSE. 72% of the test set molecules had delta distribution less than 0.5.
The accuracy report is available here.
The classification model was built using the Honma et al. dataset. The current version of the classification hERG model contains around 204k training data points.
We ran a test to validate the predictive capabilities of the classification model on the remaining test subset of around 87k data points.
Our results were compared to the performance of other commercial hERG models in the article. They are summarised below.
For both models we used Random Forest and Conformal Prediction algorithms to create them and provide the applicability domain of the prediction. The applicability domain provides information about a model's performance and accuracy.
For creating the models we used selected fingerprints (e.g. ECFP) and physico-chemical descriptors.
The applicability domain is based on the 5 most similar compounds from the training set according to ECFP-4 fingerprints and Tanimoto metrics. The error bound of the applicability domain comes from the Conformal Prediction algorithm.
ChemAxon's hERG Predictor uses the acitivity and classification models to predict the pAct value and the classification class.
In MarvinSketch hERG prediction can be done with the hERG (Beta) Plugin, which can be found under the Calculations » ADMET menu item. The current version of the plugin returns both the hERG activity and the classification class by default.
You can predict hERG activity in Playground by selecting hERG under the Explore Calculations menu. Once selected, the predicted value with its applicability domain appears next to the drawing canvas.
In Design Hub predicting hERG activity can be done with the Add Property » hERG menu item. The predicted activity value with its applicability domain appears next to the drawing canvas.
To predict hERG activity with cxcalc, use the herg-beta function. You can choose which model to use for prediction by using the -a (for the activity model) and the -c (for the classification model) options. For both options the default setting is true.
The following examples show how to use cxcalc for prediction:
cxcalc -N i herg-beta aspirine hERG_activity hERG_class 3.55 SAFE
cxcalc -N i herg-beta -c false sildenafil hERG_activity 5.41
cxcalc -N i herg-beta -a false sildenafil hERG_class SAFE
To predict hERG with Chemical Terms use the hergBeta(), hergActivityBeta() functions for activity prediction and the hergClassBeta() function for classification class prediction.
The following examples show how to use Chemical Terms for prediction:
evaluate -e "hergBeta()" aspirine 3.55
evaluate -e "hergActivityBeta()" sildenafil 5.41
evaluate -e "hergClassBeta()" aspirine SAFE
NOTE: Memory issues (slowdown, prediction failure) can be experienced when predicting hERG in Instant JChem (IJC). To overcome such issues, increase the heap memory available to IJC. For more information see the relevant memory management documentation page.
The following table summarises the availability of the hERG prediction and applicability domain features in the above mentioned products.
|Product name||hERG prediction||Applicability domain|
To access and use the hERG Predictor you need a valid ChemAxon ADMET license. Please consult us for more information on licensing.
hERG v. 1.0-beta:
hERG v. 1.0-beta-2:
hERG v. 1.0-beta-3: