COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning - Liu, Yang; Wu, You; Shen, Xiaoke; Xie, Lei (Frontiers in Bioinformatics, 2021)
The life-threatening disease Covid-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN, a new deep learning model for molecular property prediction. MolGNN applies graph neural network to computational learning of chemical molecule embeddings. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction performance without manual labels in the pretraining stage, and excellent performance on data with only a few labels. Our results indicate that MolGNN is robust to scarce training data, and hence a powerful few-shot learning tool. MolGNN predicted several multi-targeted molecules against both human Janus Kinases and the SARS-CoV-2 main protease, which are preferential targets for drugs aiming respectively at alleviating cytokine storm Covid-19 symptoms and suppressing viral replication. We also predicted molecules potentially inhibiting cell death induced by SARS-CoV-2. Several of MolGNN top predictions are supported by existing experimental and clinical evidence, demonstrating the potential value of our method.