Given the success of the RDKit descriptors alone (Fig. ) we examined the affinity predictions for ligands that bind to proteins in multiple structures in the PDBbind database. Our RDKit RF model will produce only one value so it must be "incorrect" for many of the protein-ligand complexes. When the RDKit RF model was tested on a previously-unseen ligand, having been trained on all other data in the PDBbind 2017 general set, the score was found to be strongly correlated with the mean experimental pK of that ligand for its targets across the PDBbind 2017 general set (ρ p = 0.72, Fig. ). For the most common ligands in the PDBbind 2017 general set, the reported affinity values can span several orders of magnitude, so the RDKit RF model is not simply predicting a single "correct" value for a ligand that happens to have many similar affinity measurements. Fig. shows the predictions of the RDKit RF model for the most common ligands in the PDBbind 2017 general set (markers) against the pK values reported by PDBbind (swarm plots). With the clear exception of biotin (BTN), the marker is often close to the centre of the swarm plot, indicating that the RDKit RF model appears to be predicting the mean affinity of the ligand even when the experimental data have a range of several pK units. We verified that many of the structures for biotin were biotin-streptavidin or biotin-avidin complexes, explaining the Further, we found that when nearly-identical ligands (Tanimoto similarity > 0.9) were excluded from the training data, this correlation is actually stronger (ρ p = 0.75) (Supporting Information Fig. ). Reducing the Tanimoto similarity above which ligands are excluded from the training set results in gradually weakening correlation (Supporting Information Fig. ), suggesting that the RDKit RF model is not simply memorizing the average affinity of highly similar training ligands. fore expect these features to capture useful information about the ability of a small molecule to bind to a charged, buried active site. Both these properties are also used to characterize drugs and log P is also used to predict bioavailability . It is possible that the predictive power of these features can in part be attributed to systematic bias in favour of crystallising complexes featuring high-affinity engineered compounds. However, we found that there was no trivial correlation between either of these fea- whose contribution to the model might also be through capturing the shape and surface area of the molecule or some aspect of conformational entropy. We also found that when ligand-based features were combined with structure-based features in our other models, both ligand-based and structure-based features were ranked highly, and that the same ligand-based features were consistently found to be important regardless of which structure-based features were used (Supporting Information Fig. ). This suggests that the ligand-based features are consistently capturing useful information that is not present in the structure-based features, beyond the count of rotatable bonds in the ligand.