On Nov.22, Professor Tingjun Hou, College of pharmaceutic science Zhejiang University (CPS-ZJU), Professor Shibo He, College of control science and engineering Zhejiang University, Professor Dongsheng Cao, Central South University and researchers from Tencent Quantum Laboratory published the newest research entitled A unified drug-target interaction prediction framework based on knowledge graph and recommendation system in Nature Communications.

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

This work was carried out by Ph.D. student Qing Ye, ZJU-CPS and Dr. Changyu Xie, Tencent Quantum Laboratory under the supervision of Professor Tingjun Hou, Professor Shibo He and Professor Dongsheng Cao.

Link:https://www.nature.com/articles/s41467-021-27137-3