Abstract
Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein–ligand-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. All codes are available in the https://github.com/Jthy-af/HaPPy.
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Data availability
Data openly available in a public repository. The data that support the findings of this study are openly available in PDBbind dataset at http://pdbbind.org.cn. And all codes are available in the https://github.com/Jthy-af/HaPPy or from the corresponding author on reasonable request.
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Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 22033002 and Grant 92370127, in part by National Natural Science Foundation of China under Grant 21873050.
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Zhang, X., Li, Y., Wang, J. et al. A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction. Interdiscip Sci Comput Life Sci 15, 696–709 (2023). https://doi.org/10.1007/s12539-023-00582-y
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DOI: https://doi.org/10.1007/s12539-023-00582-y