Modeling Dual-Range Atomic Interactions with Physicochemical Principles for Molecular Force Fields

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ID: 324368
2026
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Abstract
Abstract Motivation Machine Learning Force Fields (MLFFs) have emerged as promising tools for accelerating molecular dynamics simulations. However, existing approaches often struggle to capture the geometric characteristics of long-range interactions, including distance and direction, remain sensitive to conformational variations, and lack adaptive mechanisms for balancing short- and long-range forces. To address these limitations, we propose GeoNet , a physicochemical-principle-guided framework for modeling dual-range atomic interactions. GeoNet employs geometric attention over atom–fragment bipartite graphs to characterize long-range dependencies, introduces dual-level augmentation to enforce semantic consistency across molecular conformations, and uses an adaptive fusion module to dynamically balance short- and long-range interaction pathways according to local atomic environments. Results Extensive experiments show that GeoNet consistently outperforms ten state-of-the-art baselines across the evaluated benchmarks. Moreover, it achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency. Availability The source code is publicly available at https://github.com/XMUDM/GeoNet.
Reference Key
openalex_W7201962396 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Honghao Wang, Zunlong Liu, Xiangxiang Zeng, Zhaohui Song, Chen Lin
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag580
URL
https://doi.org/10.1093/bioinformatics/btag580
Keywords Keywords not found

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