Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction
The human ether-a-go-go-related (hERG) gene is crucial in enabling the regulation of repolarisation process in the heart. Some chemicals act as hERG blockers, resulting in prolonged QT intervals. Predicting the binding capability of molecules with hERG channels is expected to reduce the burden of ca...
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IEEE
2025-01-01
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| author | Syed Mohammad Vaisali Chandrasekar Omar Aboumarzouk Ajay Vikram Singh Sarada Prasad Dakua |
| author_facet | Syed Mohammad Vaisali Chandrasekar Omar Aboumarzouk Ajay Vikram Singh Sarada Prasad Dakua |
| author_sort | Syed Mohammad |
| collection | DOAJ |
| description | The human ether-a-go-go-related (hERG) gene is crucial in enabling the regulation of repolarisation process in the heart. Some chemicals act as hERG blockers, resulting in prolonged QT intervals. Predicting the binding capability of molecules with hERG channels is expected to reduce the burden of cardiotoxicity testing in drug evaluation. The application of machine learning (ML) and deep learning (DL) models in the field of toxicity has gained burgeoning interest. The current study utilises state-of-the-art ML and DL models for predicting the hERG-blocking ability of chemical compounds using a dataset of 8337 molecules. It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. While the threshold for blockers is defined as <inline-formula> <tex-math notation="LaTeX">$\leq 10~\mu $ </tex-math></inline-formula>M and for non-blockers, it is <inline-formula> <tex-math notation="LaTeX">$\gt 10~\mu $ </tex-math></inline-formula>M, our analysis indicates that a threshold of 60-<inline-formula> <tex-math notation="LaTeX">$80~\mu $ </tex-math></inline-formula>M provides a more accurate cut-off for non-blockers. This adjustment highlights the importance of concentration levels in reflecting the variability specific to individual interaction sites. The algorithm results show that the internal validation performance of RF, XGBoost, and MLP is strong, with AUC scores of 0.90, 0.90, and 0.87, respectively. In summary, the current study provides a machine learning framework for computation cardiotoxicity assessment by analysis of the hERG blocker concentration cut-offs using different fingerprints at multiple thresholds. |
| format | Article |
| id | doaj-art-699d1228c84345aa8299cd4e3c7104b5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-699d1228c84345aa8299cd4e3c7104b52025-08-20T02:58:54ZengIEEEIEEE Access2169-35362025-01-0113810188102810.1109/ACCESS.2025.356644010982226Leveraging Machine and Deep Learning Algorithms for hERG Blocker PredictionSyed Mohammad0https://orcid.org/0009-0008-1915-9640Vaisali Chandrasekar1https://orcid.org/0000-0001-7149-5459Omar Aboumarzouk2https://orcid.org/0000-0002-7961-7614Ajay Vikram Singh3https://orcid.org/0000-0002-9875-7727Sarada Prasad Dakua4https://orcid.org/0000-0003-2979-0272Department of Surgery, Hamad Medical Corporation, Doha, QatarDepartment of Surgery, Hamad Medical Corporation, Doha, QatarDepartment of Surgery, Hamad Medical Corporation, Doha, QatarGerman Federal Institute for Risk Assessment (BfR), Berlin, GermanyDepartment of Surgery, Hamad Medical Corporation, Doha, QatarThe human ether-a-go-go-related (hERG) gene is crucial in enabling the regulation of repolarisation process in the heart. Some chemicals act as hERG blockers, resulting in prolonged QT intervals. Predicting the binding capability of molecules with hERG channels is expected to reduce the burden of cardiotoxicity testing in drug evaluation. The application of machine learning (ML) and deep learning (DL) models in the field of toxicity has gained burgeoning interest. The current study utilises state-of-the-art ML and DL models for predicting the hERG-blocking ability of chemical compounds using a dataset of 8337 molecules. It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. While the threshold for blockers is defined as <inline-formula> <tex-math notation="LaTeX">$\leq 10~\mu $ </tex-math></inline-formula>M and for non-blockers, it is <inline-formula> <tex-math notation="LaTeX">$\gt 10~\mu $ </tex-math></inline-formula>M, our analysis indicates that a threshold of 60-<inline-formula> <tex-math notation="LaTeX">$80~\mu $ </tex-math></inline-formula>M provides a more accurate cut-off for non-blockers. This adjustment highlights the importance of concentration levels in reflecting the variability specific to individual interaction sites. The algorithm results show that the internal validation performance of RF, XGBoost, and MLP is strong, with AUC scores of 0.90, 0.90, and 0.87, respectively. In summary, the current study provides a machine learning framework for computation cardiotoxicity assessment by analysis of the hERG blocker concentration cut-offs using different fingerprints at multiple thresholds.https://ieeexplore.ieee.org/document/10982226/hERGmachine learningcardiotoxicityprediction |
| spellingShingle | Syed Mohammad Vaisali Chandrasekar Omar Aboumarzouk Ajay Vikram Singh Sarada Prasad Dakua Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction IEEE Access hERG machine learning cardiotoxicity prediction |
| title | Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction |
| title_full | Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction |
| title_fullStr | Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction |
| title_full_unstemmed | Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction |
| title_short | Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction |
| title_sort | leveraging machine and deep learning algorithms for herg blocker prediction |
| topic | hERG machine learning cardiotoxicity prediction |
| url | https://ieeexplore.ieee.org/document/10982226/ |
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