InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions

Accurate
quantification of protein–ligand interactions remains
a key challenge to structure-based drug design. However, traditional
machine learning (ML)-based methods based on handcrafted descriptors,
one-dimensional protein sequences, and/or two-dimensional graph representations
limit their capability to learn the generalized molecular interactions
in 3D space. Here, we proposed a novel deep graph representation learning
framework named InteractionGraphNet (IGN) to learn the protein–ligand
interactions from the 3D structures of protein–ligand complexes.
In IGN, two independent graph convolution modules were stacked to
sequentially learn the intramolecular and intermolecular interactions,
and the learned intermolecular interactions can be efficiently used
for subsequent tasks. Extensive binding affinity prediction, large-scale
structure-based virtual screening, and pose prediction experiments
demonstrated that IGN achieved better or competitive performance against
other state-of-the-art ML-based baselines and docking programs. More
importantly, such state-of-the-art performance was proven from the
successful learning of the key features in protein–ligand interactions
instead of just memorizing certain biased patterns from data.