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An accurate and interpretable bayesian classification model for prediction of HERG liability
1Discovery Chemistry, Hoffmann-La Roche, Inc. 340 Kingsland Street, Nutley, NJ 07110, USA. hongmao.sun@roche.com
Insights
A new naive Bayes classifier accurately predicts hERG channel blockers, a common cause of drug-induced QT prolongation. This tool aids in identifying cardiotoxic compounds early, reducing drug development risks.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- Drug-induced QT prolongation is a serious adverse effect leading to drug market withdrawals.
- Blockade of the hERG potassium channel is the primary cause of drug-induced QT prolongation.
- Early identification of hERG channel blockers is crucial to prevent cardiotoxicity and costly late-stage drug development failures.
Purpose of the Study:
- To develop and validate a predictive model for identifying hERG channel blockers.
- To categorize compounds as active or inactive hERG blockers using a naive Bayes classifier.
- To assess the utility of molecular descriptors in predicting hERG activity.
Main Methods:
- A naive Bayes classifier was constructed using a training set of 1979 compounds.
- The model utilized a universal, generic molecular descriptor system.
- Performance was evaluated using ROC accuracy and validation on an external test set of 66 drugs.
Main Results:
- The naive Bayes classifier achieved an ROC accuracy of 0.87 on the training set.
- The model correctly classified 58 out of 66 drugs in the external validation set.
- Cumulative probabilities provided confidence metrics for hERG blocker predictions.
Conclusions:
- The developed naive Bayes classifier effectively identifies potential hERG channel blockers.
- The combination of atom-typing descriptors and naive Bayes classification allows for model interpretability.
- This approach offers valuable insights for designing safer drug candidates with reduced hERG activity.
Abstract:
Drug-induced QT interval prolongation has been identified as a critical side-effect of non-cardiovascular therapeutic agents and has resulted in the withdrawal of many drugs from the market. As almost all cases of drug-induced QT prolongation can be traced to the blockade of a voltage-dependent potassium ion channel encoded by the hERG (the human ether-à-go-go-related gene), early identification of potential hERG channel blockers will decrease the risk of cardiotoxicity-induced attritions in the later and more expensive development stage. Presented herein is a naive Bayes classifier to categorize hERG blockers into active and inactive classes, by using a universal, generic molecular descriptor system.1 The naive Bayes classifier was built from a training set containing 1979 corporate compounds, and exhibited an ROC accuracy of 0.87. The model was validated on an external test set of 66 drugs, of which 58 were correctly classified. The cumulative probabilities reflected the confidence of prediction and were proven useful for the identification of hERG blockers. Relative performance was compared for two classifiers constructed from either an atom-type-based molecular descriptor or the long range functional class fingerprint descriptor FCFP_6. The combination of an atom-typing descriptor and the naive Bayes classification technique enables the interpretation of the resulting model, which offers extra information for the design of compounds free of undesirable hERG activity.