Traditional target-based drug development approaches are lengthy, costly, and with high failure rates. With increasingly available data regarding drugs, diseases, and drug targets, computational approaches that can integrate diverse sets of heterogeneous data sources have great potential to speed up the drug development process. Leonard Case Jr. Professor Jing Li from the Department of Computer and Data Sciences receives an NSF grant entitled “FET: CCF: Small: Computational Drug Prediction through Joint Learning” to develop advanced AI and machine learning algorithms to address fundamental questions in computational drug prediction. The project will study the relationships among drugs, diseases, and targets via powerful computational methods for rational drug repurposing.

Professor Li and his group will focus on the development of efficient and effective learning algorithms, rigorous theoretical analysis on algorithm convergence and complexity, and comprehensive evaluations using real data obtained from various databases. The approach will seamlessly integrate tensor decomposition with multi-view learning and deep learning. By utilizing auxiliary information in the framework of multi-view learning, it can effectively address the sparsity issue and the learned hidden structures should be more meaningful and interpretable.