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GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity
Yankang Jing1,2, Yiyang Zhang1,2, Guangyi Zhao1,2
1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
Insights
A new deep learning method automatically learns atom representations, improving in silico screening for hERG channel blockers. This approach enhances drug discovery by accurately identifying potential cardiotoxic compounds faster than traditional methods.
Area of Science:
- Computational chemistry and pharmacology
- Artificial intelligence in drug discovery
- Cardiovascular safety pharmacology
Background:
- The human Ether-a-go-go Related-Gene (hERG) channel is crucial for cardiac repolarization.
- hERG channel blockade by drugs can cause lethal arrhythmias like long QT syndrome.
- Current drug screening methods for hERG inhibition are inefficient and time-consuming.
Purpose of the Study:
- To develop an automated method for learning molecular representations to improve in silico hERG screening.
- To overcome limitations of traditional models relying on manually defined atomic features.
- To accelerate the identification of potential hERG inhibitors for drug safety.
Main Methods:
- Developed a deep neural network (DNN) model for automated atom embedding using 118,312 compounds from ZINC.
- Trained a Graph Neural Network (GNN) model using 7,909 ChEMBL compounds for classification.
- Integrated the atom embedding and GNN models into a classifier to distinguish hERG inhibitors from non-inhibitors.
Main Results:
- The automated atom embedding model achieved 0.93 accuracy in structural representation.
- The best performing GNN model reached 0.84 accuracy in predicting hERG inhibition.
- The GNN model outperformed traditional machine learning and existing AI-driven models in external validation.
Conclusions:
- The automated atom embedding model provides a robust standard for molecular representations.
- Integrating this model with GNNs significantly aids in screening hERG inhibitors.
- This approach accelerates drug discovery and repurposing by enhancing computational safety assessments.
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.
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