Key result
A machine learning model combining ligand-based and structure-based features achieved the best performance in predicting hERG liability on an external test set of unseen molecules, with an accuracy of 0.69 and AUC of 0.67.
Why the study?
Ligand-based computational models for hERG liability fail on new chemical scaffolds, but the recently solved 3D structure of the hERG channel enables structure-based strategies that may overcome these limitations.
Combining ligand-based and structure-based computational features improves the prediction of hERG-related cardiotoxicity for new drug candidates.
Hybrid ML models may aid early hERG screening in drug discovery; leaves open prospective validation before clinical adoption.
Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.
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Vittorio et al. (2023) studied hERG liability (Drug-induced cardiotoxicity) (n=12,789). Machine learning classification models (Ligand-based and Structure-based) vs. Comparison between Ligand-based, Structure-based, and Consensus models was evaluated on Model predictive performance (Matthews Correlation Coefficient, Accuracy, AUC) on an external test set. A machine learning model combining ligand-based and structure-based features achieved the best performance in predicting hERG liability on an external test set of unseen molecules, with an accuracy of 0.69 and AUC of 0.67.
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