Qingpeng Zhang is an Associate Professor with the School of Data Science at CityU. He received the B.S. degree in Automation from Huazhong University of Science and Technology in 2009, and the Ph.D. degrees in Systems and Industrial Engineering from The University of Arizona in 2012. Prior to joining CityU, he worked as a Postdoctoral Research Associate with The Tetherless World Constellation at Rensselaer Polytechnic Institute. His research interests include healthcare data analytics, medical informatics, network science, and artificial intelligence. His research has been published in leading journals such as Nature Human Behaviour, Nature Communications, JAMIA and MIS Quarterly, as well as featured in press such as The Washington Post, The New York Times, New York Public Radio, The Guardian, The Daily Mail,and Global News.
In this talk, I will introduce an end-to-end deep learning framework based on a protein–protein interaction (PPI) network to make synergistic anticancer drug combination predictions. The framework, namely GraphSynergy, adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxicity scores. An attention component is also introduced in GraphSynergy, which aims to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines. GraphSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the 2 latest drug combination datasets. Specifically, GraphSynergy achieves accuracy values of 0.7553 (11.94% improvement compared to DeepSynergy, the latest published drug combination prediction algorithm) and 0.7557 (10.95% improvement compared to DeepSynergy) on DrugCombDB and Oncology-Screen datasets, respectively. Furthermore, the proteins allocated with high contribution weights during the training of GraphSynergy are proved to play a role in view of molecular functions and biological processes, such as transcription and transcription regulation. This research indicates that introducing topological relations between drug combination and cell line within the PPI network can significantly improve the capability of synergistic drug combination identification.