Abstract
Individuals infected with HIV are controlled by drugs known as antiretroviral therapy by suppressing the amount of HIV in the body. Therefore, studies to predict both HIV-drug resistance and virus-drug interaction are of great importance for the sustainable effectiveness of antiretroviral drugs. A solution to this problem is provided by investigating the connection between the recently emerging geometric deep learning method and the evolutionary principles governing drug resistance to the HIV. In this study, geometric deep learning (GDL) approach is proposed to predict drug resistance to HIV, and virus-drug interaction. The drug data set in the SMILES representation was first converted to molecular representation and then to a graph representation that the GDL model could understand. Message Passing Neural Network (MPNN) transfers the node feature vectors to a different space for the training process to take place. Next, we develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is performed. The obtained results show that the proposed GDL method outperforms existing methods in predicting drug resistance in HIV with 93.3% accuracy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.