Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

By Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong. 2021

231 citations recorded by the source corpus.

Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the biomolecular structural information is not fully utilized. The essential long-range interactions among atoms are also neglected in GNN models. To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs the node-edge aggregation process to update embeddings of nodes and edges while preserving the distance and angle information among atoms. Then, PiPool is adopted to gather interactive edges with a subsequent reconstruction loss to reflect the global interactions. Exhaustive experimental study on two benchmarks verifies the superiority of SIGN.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, background): “…afﬁnity prediction from the 3D complex (McNutt et al., 2021; Fran-coeur et al., 2020; Ragoza et al., 2017; Wallach et al., 2015; Lim et al., 2019; Morrone et al., 2020; Jiang et al., 2021; Shen et al., 2021; Jastrzebski et al., 2020; Bao et al., 2021; Torng & Altman, 2019a; Li et al., 2021).”
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes (2022, methodology): “In recent works [14, 15, 18, 31], the message passing scheme has been modified to capture the angular information in a molecular graph G = (V , E) with N nodes and position vectors r = {r1, .”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, methodology): “Prior methods (Wallach et al., 2015; Li et al., 2021) predict binding afﬁnity from protein-ligand co-crystal structures, but such data is expensive to obtain experimentally.”