HERG-Att: Self-attention-based deep neural network for predicting hERG blockers. (August 2020)

• Record Type: Journal Article
• Authors: Kim, Hyunho; Nam, Hojung
• Is Part Of: Computational biology and chemistry. Volume 87(2020)
• Publication Date: 2020-08
• Subjects: hERG blockers prediction -- Deep learning -- Self-attention mechanism; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107286
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The first self-attention-based, interpretable deep neural network model for predicting hERG blockers is proposed. The various datasets ranging from public databases to publicly available private datasets are collected to train and test the model. The proposed model not only shows better performance compared to other related studies but also provides potential hERG-related substructures. Abstract: A voltage-gated potassium channel encoded by the human ether-à-go-go-related gene (hERG) regulates cardiac action potential, and it is involved in cardiotoxicity with compounds that inhibit its activity. Therefore, the screening of hERG channel blockers is a mandatory step in the drug discovery process. The screening of hERG blockers by using conventional methods is inefficient in terms of cost and efforts. This has led to the development of many in silico hERG blocker prediction models. However, constructing a high-performance predictive model with interpretability on hERG blockage by certain compounds is a major obstacle. In this study, we developed the first, attention-based, interpretable model that predicts hERG blockers and captures important hERG-related compound substructures. To do that, we first collected various datasets, ranging from public databases to publicly available private datasets, to train and test the model. Then, we developed a precise and interpretable hERG blocker prediction model by using deep learning with a self-attention approach that has an appropriate molecular descriptor, Morgan fingerprint. The proposed prediction model was validated, and the validation result showed that the model was well-optimized and had high performance. The test set performance of the proposed model was significantly higher than that of previous fingerprint-based conventional machine learning models. In particular, the proposed model generally had high accuracy and F1 score thereby, representing the model's predictive reliability. Furthermore, we interpreted the calculated attention score vectors obtained from the proposed prediction model and demonstrated the important structural patterns that are represented in hERG blockers. In summary, we have proposed a powerful and interpretable hERG blocker prediction model that can reduce the overall cost of drug discovery by accurately screening for hERG blockers and suggesting hERG-related substructures.