A-practical-benchmark-system-should-address-the-following-concerns-about-deep-learning-protein-ligand-docking-models-Molecular-docking-models-must-adapt-to-realistic-molecular-docking-tasks-such-as-binding-pose-prediction-and-virtual-screening-To-tra

A comprehensive benchmark system for deep learning protein-ligand docking models should take into account the following considerations. Firstly, these models need to be tailored to real-world docking tasks, including binding pose prediction and virtual screening. Secondly, the availability of large amounts of data is crucial for effective training and evaluation of such models. Additionally, relevant features must be extracted from the samples to be used as input by the deep learning docking model. Finally, the model's ability to generalize is essential, enabling it to accurately predict samples from diverse distributions beyond the training set.