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

By Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu and 2 more. 2021

241 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology, Graph Learning, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Octavian-Eugen Ganea
4. Hannes Stärk
5. Jianzhu Ma
6. Jian Peng
7. Jiahua Rao
8. Lagnajit Pattanaik

In Molecular Biology

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Hannes Stärk
4. Jianzhu Ma
5. Jian Peng
6. Jiahua Rao
7. Wesley Wei Qian
8. Lagnajit Pattanaik

In Pharmacology

1. Jian Peng
2. Regina Barzilay
3. Tommi S. Jaakkola
4. Jianzhu Ma

In Graph Learning

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Octavian-Eugen Ganea

In Computational Chemistry & Drug Discovery

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Hannes Stärk
4. Jian Peng
5. Wesley Wei Qian
6. Octavian-Eugen Ganea
7. Jiaqi Guan
8. Jianzhu Ma

Citations in the corpus

• TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “We compare TankBind against two state-of-the-art sequencebased methods, TransformerCPI [53] and MONN [28], two complex-based methods, IGN [38] and PIGNet [54] both requiring prior knowledge of the inter-molecular structure to predict affinity, and two structure-based methods, HOLOPTOT [23] and STAMP”
• 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).”
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023, methodology): “…our model, and compare it with two state-of-the-art sequence-based models TransCPI (Chen et al., 2020) and MONN (Li et al., 2020), one complex model IGN (Jiang et al., 2021), two structure-based model HOLOPROT (Somnath et al., 2021) and STAMP-DPI (Wang et al., 2022) and finally the base EGNN model.”