HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)
Keywords:Graph Neural Network, Binding Affinity Prediction, Data Representation
AbstractGathering 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.
How to Cite
Zhang, X., Gu, Y., Xu, G., Li, Y., Wang, J., & Yang, Z. (2023). HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16384-16385. https://doi.org/10.1609/aaai.v37i13.27052
AAAI Student Abstract and Poster Program