HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)
Abstract
Gathering information from multi-perspective graphs is an essential issue for many applications especially for proteinligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives.
Cite
Text
Zhang et al. "HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27052Markdown
[Zhang et al. "HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/zhang2023aaai-happy/) doi:10.1609/AAAI.V37I13.27052BibTeX
@inproceedings{zhang2023aaai-happy,
title = {{HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)}},
author = {Zhang, Xianfeng and Gu, Yanhui and Xu, Guandong and Li, Yafei and Wang, Jinlan and Yang, Zhenglu},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {16384-16385},
doi = {10.1609/AAAI.V37I13.27052},
url = {https://mlanthology.org/aaai/2023/zhang2023aaai-happy/}
}