OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction

By Liangzhen Zheng, Jingrong Fan, Yuguang Mu. ACS Omega, 2019

326 citations recorded by the source corpus.

Computational drug discovery provides an efficient tool for helping large-scale lead molecule screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities toward a target, a protein in general. The accuracies of current scoring functions that are used to predict the binding affinity are not satisfactory enough. Thus, machine learning or deep learning based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network model (called OnionNet) is introduced; its features are based on rotation-free element-pair-specific contacts between ligands and protein atoms, and the contacts are further grouped into different distance ranges to cover both the local and nonlocal interaction information between the ligand and the protein. The prediction power of the model is evaluated and compared with other scoring functions using the comparative assessment of scoring functions (CASF-2013) benchmark and the v2016 core set of the PDBbind database. The robustness of the model is further explored by predicting the binding affinities of the complexes generated from docking simulations instead of experimentally determined PDB structures.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1941106af14f937cf1845ef082ec9210e3ee4c92

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. Ying Wei
2. Dejing Dou
3. Junzhou Huang
4. Yatao Bian
5. Yu Rong

In Molecular Biology

1. Ying Wei
2. Dejing Dou
3. Tingyang Xu
4. Junzhou Huang
5. Yatao Bian
6. Wen-bing Huang
7. Yu Rong

Citations in the corpus

• Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction (2022, unknown): “In addition, PLIGs outperform other well-established structure-based models such as PLEC ( ρ =0.817, [10]), OnionNet ( ρ =0.816, [34]) and K DEEP ( ρ =0.82, [11]) as well as other recent GNN-based scoring functions such as SIGN ( ρ =0.797, [12]).”
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, methodology): “We compare our proposed model with comparative methods including machine learning-based methods (LR, SVR, 2http://www.csardock.org andRF-Score [2]), CNN-basedmethods (Pafnucy [35] andOnionNet [46]), and GNN models GraphDTA [30] for protein-ligand binding affinity prediction.”
• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020, background): “Therefore, supervised deep learning of graphs, especially with Graph Neural Networks(GNNs) [46, 25] have shown promising results in many tasks, such as molecular property prediction [13, 23] and virtual screening [56, 64].”