{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/structure-aware-interactive-graph-neural","title":"Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity","arxiv_id":"2107.10670","date":"2021-07-21","proceeding":null,"authors":["Shuangli Li","Jingbo Zhou","Tong Xu","Liang Huang","Fan Wang","Haoyi Xiong","Weili Huang","Dejing Dou","Hui Xiong"],"abstract":"Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the biomolecular structural information is not fully utilized. The essential long-range interactions among atoms are also neglected in GNN models. To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs the node-edge aggregation process to update embeddings of nodes and edges while preserving the distance and angle information among atoms. Then, PiPool is adopted to gather interactive edges with a subsequent reconstruction loss to reflect the global interactions. Exhaustive experimental study on two benchmarks verifies the superiority of SIGN.","url_abs":"https://arxiv.org/abs/2107.10670v1","url_pdf":"https://arxiv.org/pdf/2107.10670v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"structure-aware-interactive-graph-neural","repo_url":"https://github.com/agave233/SIGN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"graph-attention","task_name":"Graph Attention"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"protein-ligand-affinity-prediction","task_name":"Protein-Ligand Affinity Prediction"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"SIGN","rank_in_archive_order":5,"of":7,"metrics":{"RMSE":"1.316"},"uses_additional_data":false},{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"DimeNet","rank_in_archive_order":6,"of":7,"metrics":{"RMSE":"1.453"},"uses_additional_data":false},{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"GraphDTA","rank_in_archive_order":7,"of":7,"metrics":{"RMSE":"1.562"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2107.10670","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}