MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.
Predicting protein–ligand binding affinities (PLAs)
is a
core problem in drug discovery. Recent advances have shown great potential
in applying machine learning (ML) for PLA prediction. However, most
of them omit the 3D structures of complexes and physical interactions
between proteins and ligands, which are considered essential to understanding
the binding mechanism. This paper proposes a geometric interaction
graph neural network (GIGN) that incorporates 3D structures and physical
interactions for predicting protein–ligand binding affinities.
Specifically, we design a heterogeneous interaction layer that unifies
covalent and noncovalent interactions into the message passing phase
to learn node representations more effectively. The heterogeneous
interaction layer also follows fundamental biological laws, including
invariance to translations and rotations of the complexes, thus avoiding
expensive data augmentation strategies. GIGN achieves state-of-the-art
performance on three external test sets. Moreover, by visualizing
learned representations of protein–ligand complexes, we show
that the predictions of GIGN are biologically meaningful.
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