  Protein-ligand complex structures have been utilised to design benchmark
machine learning methods that perform important tasks related to drug design
such as receptor binding site detection, small molecule docking and binding
affinity prediction. However, these methods are usually trained on only ligand
bound (or holo) conformations of the protein and therefore are not guaranteed
to perform well when the protein structure is in its native unbound
conformation (or apo), which is usually the conformation available for a newly
identified receptor. A primary reason for this is that the local structure of
the binding site usually changes upon ligand binding. To facilitate solutions
for this problem, we propose a dataset called APObind that aims to provide apo
conformations of proteins present in the PDBbind dataset, a popular dataset
used in drug design. Furthermore, we explore the performance of methods
specific to three use cases on this dataset, through which, the importance of
validating them on the APObind dataset is demonstrated.
