HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning
Jocelyn Verna Siswanto, Caroline Angelina Sunarya, Agnes Calista, Alexander Agung Santoso Gunawan, Karli Eka Setiawan · 2024
This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics Program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set.