Kinase Ligand Dataset

The dataset used for training and testing is a curated merge between the KLIFS and Modi and Dunbrack dataset. These scripts have been written assuming a rather specific layout of the dataset being used for both training and testing. We assume that both training and testing data are co-located in the same directory, and the distrinction between training and testing is managed through "split files", which are just plain text documents containing lists of complexes. Complexes need to be named in the form {PDB ID}_{CHAIN ID}_{LIGAND NAME}_{OPTIONAL SUFFIX}. The dataset directory is assumed to contain sub-directories of each complex. Each sub-directory should contain 2 files: a protein file, and a ligand file. The protein files should contain the chain of the apo protein that actually binds to the ligand, while the ligand file should contain all the HETATM entries consisting of the ligand. The protein file should be named using the scheme {PDB ID}_protein.pdb, while the ligand file should be named like {PDB ID}_ligand.pdb. Each complex sub-directory should only contain one protein chain and one ligand, so higher order complexes need to be broken up accordingly. An example dataset directory might look like this: my_dataset/8ssp_A_627_510/8ssp_protein.pdb my_dataset/8ssp_A_627_510/8ssp_ligand.pdb my_dataset/8ssp_A_PDO_507/8ssp_protein.pdb my_dataset/8ssp_A_PDO_507/8ssp_ligand.pdbOur preprocessing scripts also take care of the problem of hydrogenation. Because most crystal structures lack hydrogens, there are some ambiguities in how the ligand file should be loaded, and RDKit, the library DiffDock uses for this purpose, often makes mistakes. In particular, RDKit doesn't seem readily able to distinguish between aromatic and cycloalkane rings in PDB files, which can have a profound impact on model performance. Our preprocessing scripts take care of this problem by explicitly adding hydrogens to the ligand and converting it to an SDF file.