Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

ABSTRACT

Background

Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade.

Study design and method

Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets.

Results

The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0–0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112–0.220).

Conclusions

The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.

KEYWORDS:

• Cardiotoxicity
• hERG channel
• machine learning
• deep learning
• prediction
• safety assessment
• model performance

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Author contribution statement

All authors made substantial contributions to the conception and design of the model development or the analysis and interpretation of the results. J Liu and K Khan conducted the model development, prediction results analysis and interpretation, and prepared a draft of the manuscript. W Guo, F Dong, W Ge, C Zhang, and P Gong participated in the data collection or discussion of model training and validations. T Patterson performed design and preparation of the manuscript. H Hong performed the conception, design, and supervision of this study and the manuscript.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17425255.2024.2377593.

Acknowledgments

This research was supported in part by an appointment to the Research Participation Program at the National Center for Toxicological Research (K Khan), administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy and the US Food and Drug Administration. This article reflects the views of the authors and does not necessarily reflect those of the US Food and Drug Administration or US Army Engineer Research and Development Center.

Additional information

Funding

This paper was not funded.