In drug discovery and repurposing, systematic analysis of genome-wide gene expression of chemical perturbations on human cell lines is a useful approach, but is limited due to a relatively low experimental throughput. Computational, deep learning methods can help. In this work a graph neural network called Deep Chemical Expression is developed that can predict chemical-induced gene expression profiles. It is applied to identify drug repurposing candidates for COVID-19 treatments.
- Thai-Hoang Pham
- Yue Qiu
- Ping Zhang
