HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student 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.

Authors

• Xianfeng Zhang Nanjing Normal University, Nanjing, China
• Yanhui Gu School of Computer and Electronic Information Science, Nanjing Normal University
• Guandong Xu University of Technology Sydney The Education University of Hong Kong
• Yafei Li Zhengzhou University
• Jinlan Wang School of Physics, Southeast University
• Zhenglu Yang Nankai University, Tianjin, China

Keywords

• Binding Affinity Prediction
• Data Representation
• Graph Neural Network