Assessing Interaction Recovery of Predicted Protein-Ligand Poses
Abstract
The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.
Cite
Text
Errington et al. "Assessing Interaction Recovery of Predicted Protein-Ligand Poses." NeurIPS 2024 Workshops: FM4Science, 2024.Markdown
[Errington et al. "Assessing Interaction Recovery of Predicted Protein-Ligand Poses." NeurIPS 2024 Workshops: FM4Science, 2024.](https://mlanthology.org/neuripsw/2024/errington2024neuripsw-assessing/)BibTeX
@inproceedings{errington2024neuripsw-assessing,
title = {{Assessing Interaction Recovery of Predicted Protein-Ligand Poses}},
author = {Errington, David and Schneider, Constantin and Bouysset, Cédric and Dreyer, Frederic A},
booktitle = {NeurIPS 2024 Workshops: FM4Science},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/errington2024neuripsw-assessing/}
}