Finding new drugs in the seas of small molecules is a difficult task if no prior information is available. Our broad research goal is to develop innovative and accurate machine learning algorithms to predict the drug responses related to complex human diseases. Specifically, we pursue questions of how a cell line responds to a single drug and combinatorial therapies, from the perspective of biological networks and small-molecule chemoinformatics. One research goal is to understand and predict the cell line-specific responses through integrating a wide range of methods, including the propagation of drug effects via biological networks, matrix factorization of molecular profiles and chemoinformatic analysis of small molecules. We will deploy our algorithms to softwares and web servers, which will inform the downstream experimental design to identify the single and combinatorial drug candidates against human diseases. Our research program will contribute to accelerate the drug discovery process by in silico screening through large amount of potent chemicals.
Molecular targeted therapy is one of the most successful weapons against many complex human diseases. However, identifying effective small-molecule drugs is like looking for a needle in a haystack. The proposed research, by bridging the gaps between existing methods and creating novel machine learning algorithms, will provide valuable guidance to drug development.