Figure 2. Comparison of EF_1% results obtained from classical SFs: D_score, Chemscore, G_score, PMF_score, native score (i.e. which was used to by docking software), with results from three versions of RF-Score-VS.

Unlike RF-Score-VS, RF-Score v3 does not train on any negative data (this SF for binding affinity prediction was exclusively trained on X-ray crystal structures¹²). Each boxplot shows five EF_1% values for a given SF resulting from the five 80:20 data partitions (i.e. five non-overlapping test sets collectively comprising all data). All train-test splitting scenarios are present, namely vertical, horizontal and per-target. A dramatic increase in machine-learning scoring performance (measured as EF_1%) can be seen in RF-Score-VS compared to classical SFs.