For ChEMBL datasets we used a similar approach but found it necessary to use feature constrained docking instead of MCS docking. Our aim is to increase the size of the datasets but not at any cost, instead we want to only retain high confidence docked solutions that remain close in 3D to experimental solutions. In brief, the ChEMBL data comprises examples of LO series from medicinal chemistry literature but there are fewer actives per scaffold than compared with a full internal LO dataset. Also, similar but distinct chemical scaffolds from different publications can be seen to share binding modes but not necessarily a substantial MCS. Consider for example a change in a nitrogen position in an aromatic scaffold of a kinase inhibitor, wherein similar substituents and the hinge binding motif are maintained despite no substantial MCS. Thus, upon testing we found that docking with interaction restraints was far better at returning the expected overlapping and high confidence docking results. For each target we identified interactions that are most repeated and well stablished for the given protein (i.e: kinase hinge hydrogen bond acceptor and donor/s). The sequence motifs in which the interactions are located were used to identify the residues taking part in the interactions across the different PDB structures as residue and atom numbers are usually not consistent. Constrained docking was then performed over all available structures, keeping the complex with better docking score per ligand docking solution, visual inspection was performed to remove remaining problematic docked structures. Finally, macrocycles were identified and removed from the dataset we well as compounds with poor docking scores. The docking score threshold for each dataset was selected based on the distribution of scores, thereby removing those at the tail of worse score.