MIT's ComboNet approach utilizes a graph convolutional network that embeds a molecule into a continuous feature vector. This vector is learned automatically through the single-agent and combination data rather than hand-crafted. When trained on the single-agent data, the model takes a molecule as input and predicts whether it will inhibit cancer cell growth (binary label). When trained on the combination data, the model first takes a combination (A, B) as input and predicts the inhibition score c(AB) of this combination. Then it predicts their single-agent activity s(A) and s(B) and computes its expected inhibition score under bliss independence assumption: s(AB) = s(A) + s(B) -s(A)s(B). Finally, it quantifies the synergy score of combination (A, B) as c(AB)s(AB). In addition to pancreatic cancer (PANC-1), our model is trained on 40 different cell lines collected from NCI-60 (single-agent data) and NCI ALMANAC (combination data) .