Figure 3: Comparison of EF1% results from Per-Target and Horizontal-split models.
From: Performance of machine-learning scoring functions in structure-based virtual screening
Each data point is a separate corresponds to the performance of both models on a particular DUD-E target. The darker is the colour of DUD-E target is, the more active ligands it has. Docking conformations were obtained from Autodock Vina. Dashed red line denotes equal performance, and dotted green line show 5-unit intervals. For most targets and contrary to common assumption, there is little advantage in training machine machine-learning SFs for per-target vs using a more generic approach (in this case horizontal split), especially for targets with greater number of active molecules.
