First, we performed the analysis of the PANC-1 training dataset. Figure shows activity distribution of the 32 compounds in single-agent dose response curves used for modeling. The IC50 values range between 2 nM and 3 mM, which points out that the dataset is diverse in terms of the activity values. Next, all possible pairwise combinations of 32 compounds were plated and screened in duplicates in 10×10 matrix format utilizing the PANC-1 cell line assay for generation of representative modeling set. We further analyzed the reproducibility of assay results and the best metrics to identify synergistic drug combinations. For this, we calculated the Pearson correlation of the gamma scores, beta, and Excess HSA. The correlation of gamma scores of duplicates was higher than the other two metrics (Figure , Supplementary Figure ), confirming a great reproducibility of utilized assay system and matrix screening technology. Hence, gamma was used as the synergy metric with a cutoff of 0.95 (𝛾𝛾 ≤ 0.95 as synergistic and 𝛾𝛾 > 0.95 as nonsynergistic or antagonist) . The gamma scores were averaged for each duplicate resulting in one synergy value for each unique combination. The original library of 1784 compounds, after omitting the 32 compounds, was used to generate 1,533,876 (all possible) pairwise combinations that serves as the virtual data set to predict prospectively and select new synergistic combination for further biological validation. Three modeling approaches were then pursued by three independent research groups and each approach provided a ranked list of top 30 synergistic combinations from the generated virtual set.