Ensemble graph neural networks for structural classification of HIV inhibiting molecules
International Journal of Information Technology (Singapore), ISSN: 2511-2112, Vol: 17, Issue: 2, Page: 895-909
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Graph Neural Networks (GNN) have proved to be extremely effective in molecular machine learning in the past few years. Identifying whether a molecule can inhibit the Human Immuno-Deficiency Virus (HIV) based on its structure is one such task that can be performed by harnessing the power of graph neural networks. This paper presents an ensemble graph neural network based approach to classify molecules as HIV inhibitors by using graph attention networks and graph convolutional networks. Graph representations are constructed from these molecules and are used for training the proposed GNN models. The ensemble GNN model achieves a test accuracy of 86.4% along with a high precision value for both the classes-HIV inhibitors and non-inhibitors. This paper further shows that ensemble models can be used in GNNs to overcome the problems related to biasing of models towards a particular class specifically for imbalanced classification tasks which are common in medical and bioinformatics datasets.
Bibliographic Details
Springer Science and Business Media LLC
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