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Protein-ligand complex structures have been utilised to design benchmarkmachine learning methods that perform important tasks related to drug designsuch as receptor binding site detection, small molecule docking and bindingaffinity prediction. However, these methods are usually trained on only ligandbound (or holo) conformations of the protein and therefore are not guaranteedto perform well when the protein structure is in its native unboundconformation (or apo), which is usually the conformation available for a newlyidentified receptor. A primary reason for this is that the local structure ofthe binding site usually changes upon ligand binding. To facilitate solutionsfor this problem, we propose a dataset called APObind that aims to provide apoconformations of proteins present in the PDBbind dataset, a popular datasetused in drug design. Furthermore, we explore the performance of methodsspecific to three use cases on this dataset, through which, the importance ofvalidating them on the APObind dataset is demonstrated.

APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design