We also conduct an experiment which extends the BCR-Abl protein inhibitor design application to the multi-objective optimization setting. Here, we task Atlas with identifying inhibitors with a polypharmacologial effect. In other words, identifying a single drug molecule that is able to act on multiple targets or disease pathways. The well-studied tyrosine kinase inhibitor Imatinib was originally designed as a selective inhibitor for the BCR-Abl fusion protein to treat chronic myeloid lukemia, but was later shown to inhibit nononcogenic C-Abl kinase in normal cells. Indeed, it is Imatinib's polypharmacological profile that has been suggested to form the basis of its therapeutic activity. Protein kinase inhibitors that exhibit polypharmacology thus can have improved efficacy and the potential to treat multiple types of cancer, but may come with the added risk of serious side effects. We create two additional virtual objectives by extrapolating the IC 50 of the inhibitors reported by Desai et al. for two additional tyrosine kinase receptors: the platelet-derived growth factor receptor (PDGF) and stem cell growth factor (KIT). Datasets of IC 50 values for tyrosine kinase inhibitor drugs against both KIT and PDGF were procured from Bind-ingDB. We train message-passing neural networks (MPNNs) on each dataset using the Chemprop framework, which achieve satisfactory performance (test set Pearson coefficients ≥ 0.88). The trained MPNN models then predict values for the Desai et al. inhibitors, which are used as lookup table objectives in our multiobjecitve optimization experiments. Additional details on the construction of the multi-objective problem can be found in SI Sec. S.1 C 2-S.1 C 3. These tests use the same synthesizability constraint function as do the previous single objective experiments. Multi-objective optimization strategies seek to maximize the dominated hypervolume of the objective space by using the hypervolume indicator as a scalarizing function. In other words, strategies seek to simultaneously minimize the IC 50 values for all three receptors. Results of these experiments are tabulated in Tab. II. Numerical values report the percentage of the parameter space measured for strategies to identify the entire Pareto set, i.e. the set of inhibitors for which improvement of one receptor's IC 50 value is not possible without simultaneously deteriorating another's value. In this dataset, the Pareto set consists of 13 inhibitors.