Fig. 5. The Q.E.D model performance as a function of training data size and scope.

a The drop-out experiment removed increasing numbers of training compounds (as measured by maximum Tanimoto similarity with ECFP4 fingerprint between each training compound and all Round 2 test set compounds), retrained the Q.E.D model, and tested the performance. AD stands for all data. A noticeable decrease in performance begins to appear only at around 0.6 Tanimoto similarity suggesting that highly similar compounds in the training dataset are not necessarily required for accurate model performance. As a control, identical numbers of random compound-kinase pairs were removed, repeated 5 times to assess the variability of random removal. The error bars indicate the standard deviation of these replicates. Black points indicate proportions of removed compound-kinase pairs. b A histogram describing the full training dataset used to generate the results in a. c Model performance with multiple training datasets and varying pK_d levels, where the ranges in the x-axis labels refer to the compound-kinase pairs that were included for the model training. AD stands for all data. Random dropout control was repeated 5 times. The error bars indicate the standard deviation of these replicates. d A histogram describing the full training dataset used to generate the results in c. Source data are provided as a Source Data file⁵⁴.