As mentioned previously, this result does not affect our machine learning method since we have classified molecules based on high and low potency rather than estimating the specific IC50 value. A possible explanation of this difference in experimental results between our work and the patent could be differences in protocols used to perform the HTRF assay and calculation of IC50 values. For this reason, we have included a detailed account of HTRF assay protocol, analysis of data for calculation of IC50 and supporting data files to be used by the scientific community (see Experimental Section). In order to test the validity of our bootstrapped EGNN model to correctly identify low potency predictions, we also tested compounds 4c, 4d and 4e resulting in no/poor inhibition to PD-1/PD-L1 interaction (Table ). The IC50 plots for each compound tested (Figure ) as well as the C and 1 H NMR spectra are provided as Supporting Information. Pairwise Tanimoto similarity scores between these 4a-4e (Table ) show that the EGNN model's capability of identifying high potency and low potency inhibitors regardless of the structural similarity. The compound 4e shows a high similarity with the control BMS compound (4e) with a Tanimoto similarity score of 0.8018. However, the model recognized it as a low potency molecule and the actual test showed that it is a poor inhibitor for PD1/PD-L1 with an IC50 of 1261 nM. On the other hand, the model recognized compound 4b as a high potency PD1/PD-L1 inhibitor and the HTRF assay confirmed it with a very good IC50 of 339.9 nM. However, it's pairwise Tanimoto similarity score with the control compound (4a) is only 0.5074. Taken together, these results suggest that the bootstrapped EGNN model can be used to select molecules for synthesis and experimental validation of PD-1/PD-L1 inhibition and it can identify low potency molecules which are structurally similar to the control compound 4a.