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

arXiv: https://arxiv.org/abs/1910.02845 · HTML: https://mlsanity.com/paper/1910.02845/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Tx-LLM: A Large Language Model for Therapeutics](https://mlsanity.com/paper/2406.06316/) (2024)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Assigning Confidence to Molecular Property Prediction](https://mlsanity.com/paper/2102.11439/) (2021)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation](https://mlsanity.com/paper/2107.08987/) (2021)
- [FlexVDW: A machine learning approach to account for protein flexibility in ligand docking](https://mlsanity.com/paper/2303.11494/) (2023)
- [Multi-view biomedical foundation models for molecule-target and property prediction](https://mlsanity.com/paper/2410.19704/) (2024)
- [Recent Developments in Structure-Based Virtual Screening Approaches](https://mlsanity.com/paper/2211.03208/) (2022)
- [Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats](https://mlsanity.com/paper/2308.01921/) (2023)

## Abstract (arXiv)

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