Structural Activity Prediction Models Recover Known Binding Modes (Poster Abstract)
Abstract
Drug discovery can benefit from machine learning on 3D structural data of protein-ligand (PL) complexes, but a shortage of such data limits model training. For kinase targets, we generated a large in-silico dataset, kinodata-3D, using template docking. This dataset improved binding affinity predictions. Using an E(3)-invariant GNN model, we investigated learned protein-ligand interactions by removing spatial edges between protein and ligand atoms. Significant prediction changes in known binding regions confirmed the model's understanding of binding mechanisms. This approach aims to enhance explainable AI (XAI) methods, aiding the discovery of novel kinase binding mechanisms and improving model transparency.
Cite
Text
Backenköhler et al. "Structural Activity Prediction Models Recover Known Binding Modes (Poster Abstract)." ICML 2024 Workshops: ML4LMS, 2024.Markdown
[Backenköhler et al. "Structural Activity Prediction Models Recover Known Binding Modes (Poster Abstract)." ICML 2024 Workshops: ML4LMS, 2024.](https://mlanthology.org/icmlw/2024/backenkohler2024icmlw-structural/)BibTeX
@inproceedings{backenkohler2024icmlw-structural,
title = {{Structural Activity Prediction Models Recover Known Binding Modes (Poster Abstract)}},
author = {Backenköhler, Michael and Groß, Joschka and Kramer, Paula Linh and Wolf, Verena and Volkamer, Andrea},
booktitle = {ICML 2024 Workshops: ML4LMS},
year = {2024},
url = {https://mlanthology.org/icmlw/2024/backenkohler2024icmlw-structural/}
}