HIV-1/HBV Coinfection Accurate Multitarget Prediction Using a Graph Neural Network-Based Ensemble Predicting Model

HIV and HBV infection are both serious public health challenges. There are more than approximately 4 million patients coinfected with HIV and HBV worldwide, and approximately 5% to 15% of those infected with HIV are coinfected with HBV. Disease progression is more rapid in patients with coinfection,...

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Bibliographic Details
Published in:International journal of molecular sciences 2023-04, Vol.24 (8), p.7139
Main Authors: Wang, Yishu, Li, Yue, Chen, Xiaomin, Zhao, Lutao
Format: Article
Language:English
Subjects:
AIDS treatment
Anti-HIV agents
Antiviral agents
Coinfection - complications
Coinfection - drug therapy
Comorbidity
Darunavir
drug discovery
gradient boosting decision tree
graph neural network
Health aspects
Hepatitis B
Hepatitis B virus
HIV (Viruses)
HIV Infections - complications
HIV Infections - drug therapy
HIV patients
HIV-1
HIV-1/HBV coinfection
Humans
Liver cirrhosis
Machine learning
multitarget prediction
Neural networks
Neural Networks, Computer
Reverse transcriptase inhibitors
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Summary:HIV and HBV infection are both serious public health challenges. There are more than approximately 4 million patients coinfected with HIV and HBV worldwide, and approximately 5% to 15% of those infected with HIV are coinfected with HBV. Disease progression is more rapid in patients with coinfection, which significantly increases the likelihood of patients progressing from chronic hepatitis to cirrhosis, end-stage liver disease, and hepatocellular carcinoma. HIV treatment is complicated by drug interactions, antiretroviral (ARV) hepatotoxicity, and HBV-related immune reconditioning and inflammatory syndromes. Drug development is a highly costly and time-consuming procedure with traditional experimental methods. With the development of computer-aided drug design techniques, both machine learning and deep learning have been successfully used to facilitate rapid innovations in the virtual screening of candidate drugs. In this study, we proposed a graph neural network-based molecular feature extraction model by integrating one optimal supervised learner to replace the output layer of the GNN to accurately predict the potential multitargets of HIV-1/HBV coinfections. The experimental results strongly suggested that DMPNN + GBDT may greatly improve the accuracy of binary-target predictions and efficiently identify the potential multiple targets of HIV-1 and HBV simultaneously.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms24087139