In this study, we combined efforts from three independent groups representing the National Center for Advancing Translational Sciences (NCATS), The University of North Carolina at Chapel Hill (UNC), and the Massachusetts Institute of Technology (MIT) to utilize different AI/ML methodologies to discover new prospective drug combinations against pancreatic cancer. To enable computational modeling studies, the NCATS team performed cell-based assays against pancreatic cancer, screening 1912 single agent compounds as well as assessing the most active 32 compounds (determined by IC50 and curve response class) for binary combinations using a 10 × 10 matrix format. Each team trained their models independently using 496 drug combinations in the training set with measured synergy effects on pancreatic cancer cell lines (see below for details of data generation) and predicted the top 30 synergistic combinations from 1.5 million possible combinations. Interestingly, among the proposed combinations from the three groups, there was almost no overlap (only two combos from 90), which emphasizes the importance of different AI/ML approaches. A cumulative selection of 88 drug combinations were then tested in a cellbased assay. This experimental validation resulted in 53 synergistic active combinations (out of 88 nominations; hit rate ~60%). We found that the graph convolutional method provided the best performance, with the hit rate ~83% and Random Forest showed the best precision result of 65%.