OnionNet: a multiple-layer inter-molecular contact based convolutional neural network for protein-ligand binding affinity prediction

TL;DR

OnionNet is a deep CNN model that predicts protein-ligand binding affinity using inter-molecular contact features, showing improved accuracy over previous methods and robustness in docking scenarios.

Contribution

This paper introduces OnionNet, a novel CNN model utilizing contact-based features for more accurate protein-ligand binding affinity prediction.

Findings

Improved correlation (R) and standard deviation (SD) metrics over previous models.

Enhanced robustness in docking-generated complex predictions.

Validated on CASF-2013 benchmark and PDBbind database.

Abstract

Computational drug discovery provides an efficient tool helping large scale lead molecules screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities towards a target, a protein in general. The accuracies of current scoring functions which are used to predict the binding affinity are not satisfactory enough. Thus, machine learning (ML) or deep learning (DL) based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network (CNN) model (called OnionNet) is introduced and the features are based on rotation-free element-pair specific contacts between ligands and protein atoms, and the contacts were further grouped in different distance ranges to cover both the local and non-local 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 PDBbind database. When compared to a previous CNN-based scoring function, our model shows improvements of 0.08 and 0.16 in the correlations (R) and standard deviations (SD) of regression, respectively, between the predicted binding affinities and the experimental measured binding affinities. 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.