{"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/a-universal-framework-for-accurate-and-1","title":"A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems","arxiv_id":"2311.11228","date":"2023-11-19","proceeding":"Scientific Reports 2023 11","authors":["Shuo Zhang","Yang Liu","Lei Xie"],"abstract":"Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications.","url_abs":"https://arxiv.org/abs/2311.11228v1","url_pdf":"https://arxiv.org/pdf/2311.11228v1.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":"a-universal-framework-for-accurate-and-1","repo_url":"https://github.com/XieResearchGroup/Physics-aware-Multiplex-GNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-discovery-on-qm9","task":"Drug Discovery","dataset":"QM9","model":"PAMNet","rank_in_archive_order":1,"of":11,"metrics":{"Error ratio":"0.363"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-qm9","task":"Formation Energy","dataset":"QM9","model":"PAMNet","rank_in_archive_order":3,"of":18,"metrics":{"MAE":"0.136"},"uses_additional_data":false},{"leaderboard":"/sota/protein-ligand-affinity-prediction-on-pdbbind","task":"Protein-Ligand Affinity Prediction","dataset":"PDBbind","model":"PAMNet","rank_in_archive_order":3,"of":7,"metrics":{"RMSE":"1.263"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.11228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11228"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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