The assessment of protein–ligand interactions is critical at early stage of drug discovery.
Computational approaches for efficiently predicting such interactions facilitate drug
development. Recently, methods based on deep learning, including structure-and sequence-
based models, have achieved impressive performance on several different datasets.
However, their application still suffers from a generalizability issue because of insufficient
data, especially for structure based models, as well as a heterogeneity problem because of …