Pharmaceutical research · 2025
GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.
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Jing Yankang, Zhang Yiyang, Zhao Guangyi, McGuire Terence, Zhao Jack, Gibbs Ben, Hou Ganqian, Feng Zhiwei, Xue Ying, Xie Xiang-Qun
Аннотация
PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syndrome, significantly increasing the risk of proarrhythmic cardiotoxicity. Traditional hERG screening methods are effort-demanding and time-consuming. Thus, it is essential to develop computational methods to utilize the existing knowledge for faster and more accurate in silico screening. Although with wide use of deep learning/machine learning algorithms, existing computational models often rely on manually defined atomic features to represent atom nodes, which may overlook critical underlying information. Thus, we want to provide a new method to learn the atom representation automatically.
METHODS: We first developed an automated atom embedding model using deep neural networks (DNNs), trained with 118,312 compounds collected from the ZINC database. We then trained a Graph neural networks (GNNs) model with 7909 ChEMBL compounds as the classifying part. The integration of our atom embedding model and GNN models formed a classifier that could effectively distinguish between hERG inhibitors and non-inhibitors.
RESULTS: Our atom embedding model achieved 0.93 accuracy in representing structures. Our best GNN model achieved an accuracy of 0.84 and outcompeted traditional machine-learning models, as well as published AI-driven models, in external testing.
CONCLUSIONS: These results highlight the potential of our automated atom embedding model as a standard for generating robust molecular representations. Its integration with advanced GNN algorithms offers promising assistance for screening hERG inhibitors and accelerating drug discovery and repurposing.
METHODS: We first developed an automated atom embedding model using deep neural networks (DNNs), trained with 118,312 compounds collected from the ZINC database. We then trained a Graph neural networks (GNNs) model with 7909 ChEMBL compounds as the classifying part. The integration of our atom embedding model and GNN models formed a classifier that could effectively distinguish between hERG inhibitors and non-inhibitors.
RESULTS: Our atom embedding model achieved 0.93 accuracy in representing structures. Our best GNN model achieved an accuracy of 0.84 and outcompeted traditional machine-learning models, as well as published AI-driven models, in external testing.
CONCLUSIONS: These results highlight the potential of our automated atom embedding model as a standard for generating robust molecular representations. Its integration with advanced GNN algorithms offers promising assistance for screening hERG inhibitors and accelerating drug discovery and repurposing.