Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction

TL;DR

This paper introduces a graph-based deep learning model that combines docking pose ranking and structural information to improve protein-ligand binding mode prediction, outperforming traditional docking methods especially on unbiased datasets.

Contribution

The study presents a novel dual-graph neural network architecture that integrates docking scores and structural data, enhancing binding mode prediction accuracy beyond existing methods.

Findings

Model outperforms baseline docking in various tests

Network learns from structural information on unbiased datasets

Predictions provide reliable confidence measures

Abstract

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate sub-networks for the ligand bonded topology and the ligand-protein contact map. This network division allows contributions from ligand identity to be distinguished from effects of protein-ligand interactions on classification. We show, in agreement with recent literature, that dataset bias drives many of the promising results on virtual screening that have previously been reported. However, we also show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased dataset is constructed. We develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms the baseline docking program in a variety of tests, including on cross-docking datasets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence