hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses
Abstract The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have...
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2025-01-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00957-x |
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| author | Dohyeon Lee Sunyong Yoo |
| author_facet | Dohyeon Lee Sunyong Yoo |
| author_sort | Dohyeon Lee |
| collection | DOAJ |
| description | Abstract The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have been developed to predict hERG blockers, which can efficiently save time and resources. However, previous methods have found it hard to achieve high performance and to interpret the predictive results. To overcome these challenges, we have proposed hERGAT, a graph neural network model with an attention mechanism, to consider compound interactions on atomic and molecular levels. In the atom-level interaction analysis, we applied a graph attention mechanism (GAT) that integrates information from neighboring nodes and their extended connections. The hERGAT employs a gated recurrent unit (GRU) with the GAT to learn information between more distant atoms. To confirm this, we performed clustering analysis and visualized a correlation heatmap, verifying the interactions between distant atoms were considered during the training process. In the molecule-level interaction analysis, the attention mechanism enables the target node to focus on the most relevant information, highlighting the molecular substructures that play crucial roles in predicting hERG blockers. Through a literature review, we confirmed that highlighted substructures have a significant role in determining the chemical and biological characteristics related to hERG activity. Furthermore, we integrated physicochemical properties into our hERGAT model to improve the performance. Our model achieved an area under the receiver operating characteristic of 0.907 and an area under the precision-recall of 0.904, demonstrating its effectiveness in modeling hERG activity and offering a reliable framework for optimizing drug safety in early development stages. Scientific contribution: hERGAT is a deep learning model for predicting hERG blockers by combining GAT and GRU, enabling it to capture complex interactions at atomic and molecular levels. We improve the model's interpretability by analyzing the highlighted molecular substructures, providing valuable insights into their roles in determining hERG activity. The model achieves high predictive performance, confirming its potential as a preliminary tool for early cardiotoxicity assessment and enhancing the reliability of the results. |
| format | Article |
| id | doaj-art-43b43a824d034e95a5106642e46143db |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-43b43a824d034e95a5106642e46143db2025-02-02T12:40:15ZengBMCJournal of Cheminformatics1758-29462025-01-0117111410.1186/s13321-025-00957-xhERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analysesDohyeon Lee0Sunyong Yoo1Department of Intelligent Electronics and Computer Engineering, Chonnam National UniversityDepartment of Intelligent Electronics and Computer Engineering, Chonnam National UniversityAbstract The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have been developed to predict hERG blockers, which can efficiently save time and resources. However, previous methods have found it hard to achieve high performance and to interpret the predictive results. To overcome these challenges, we have proposed hERGAT, a graph neural network model with an attention mechanism, to consider compound interactions on atomic and molecular levels. In the atom-level interaction analysis, we applied a graph attention mechanism (GAT) that integrates information from neighboring nodes and their extended connections. The hERGAT employs a gated recurrent unit (GRU) with the GAT to learn information between more distant atoms. To confirm this, we performed clustering analysis and visualized a correlation heatmap, verifying the interactions between distant atoms were considered during the training process. In the molecule-level interaction analysis, the attention mechanism enables the target node to focus on the most relevant information, highlighting the molecular substructures that play crucial roles in predicting hERG blockers. Through a literature review, we confirmed that highlighted substructures have a significant role in determining the chemical and biological characteristics related to hERG activity. Furthermore, we integrated physicochemical properties into our hERGAT model to improve the performance. Our model achieved an area under the receiver operating characteristic of 0.907 and an area under the precision-recall of 0.904, demonstrating its effectiveness in modeling hERG activity and offering a reliable framework for optimizing drug safety in early development stages. Scientific contribution: hERGAT is a deep learning model for predicting hERG blockers by combining GAT and GRU, enabling it to capture complex interactions at atomic and molecular levels. We improve the model's interpretability by analyzing the highlighted molecular substructures, providing valuable insights into their roles in determining hERG activity. The model achieves high predictive performance, confirming its potential as a preliminary tool for early cardiotoxicity assessment and enhancing the reliability of the results.https://doi.org/10.1186/s13321-025-00957-xDrug toxicityCardiotoxicityhERG channel blockerGraph neural networkGraph attention mechanism |
| spellingShingle | Dohyeon Lee Sunyong Yoo hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses Journal of Cheminformatics Drug toxicity Cardiotoxicity hERG channel blocker Graph neural network Graph attention mechanism |
| title | hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses |
| title_full | hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses |
| title_fullStr | hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses |
| title_full_unstemmed | hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses |
| title_short | hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses |
| title_sort | hergat predicting herg blockers using graph attention mechanism through atom and molecule level interaction analyses |
| topic | Drug toxicity Cardiotoxicity hERG channel blocker Graph neural network Graph attention mechanism |
| url | https://doi.org/10.1186/s13321-025-00957-x |
| work_keys_str_mv | AT dohyeonlee hergatpredictinghergblockersusinggraphattentionmechanismthroughatomandmoleculelevelinteractionanalyses AT sunyongyoo hergatpredictinghergblockersusinggraphattentionmechanismthroughatomandmoleculelevelinteractionanalyses |