We are pioneering new ways to increase efficiency and productivity of drug discovery. We developed Chem2Bio2RDF, the first large scale semantic linked data repository for preclinical drug discovery, novel link prediction and data mining algorithms for finding hidden insights in large heterogeneous data networks, and in our 2012 Drug Discovery Today paper laid out a strategy for using linked data and graph analytics to expand beyond the current single-target drug discovery model. Current projects include researching knowledge networks that encode computable networks for multi-mechanism complex diseases, integrating patient medical records with molecular data to help identify potential targeted therapies, and developing new ways to apply machine learning on top of massive heterogeneous linked data structures. We are thankful to NIH NCATS, Indiana CTSI, the OpenPHACTS foundation, Eli Lilly, and Pfizer for funding of this work. Applications in this area are being commercialized in our company Data2Discovery Inc.