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Geometric Graph Neural Diffusion for Stable Molecular Dynamics

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Machine learning force fieldgraph neural network
Abstract:

Geometric graph neural networks (Geo-GNNs) have revolutionized molecular dynamics (MD) simulations by providing accurate and fast energy and force predictions. However, minor prediction errors could still destabilize MD trajectories in real MD simulations due to the limited coverage of molecular conformations in training datasets. Existing methods that focus on in-distribution predictions often fail to address extrapolation to unseen conformations, undermining the simulation stability. To tackle this, we propose Geometric Graph Neural Diffusion (GGND), a novel framework that can capture geometrically invariant topological features, thereby alleviating error accumulation and ensuring stable MD simulations. The core of our framework is that it iteratively refines atomic representations, enabling instantaneous information flow between arbitrary atomic pairs while maintaining equivariance. Our proposed GGND is a plug-and-play module that can seamlessly integrate with existing local equivariant message-passing frameworks, enhancing their predictive performance and simulation stability. We conducted sets of experiments on the 3BPA and SAMD23 benchmark datasets, which encompass diverse molecular conformations across varied temperatures. We also ran real MD simulations to evaluate the stability. GGND outperforms baseline models in both accuracy and stability under significant topological shifts, advancing stable molecular modeling for real-world applications.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Geometric Graph Neural Diffusion (GGND), a framework that enhances molecular dynamics simulation stability by capturing geometrically invariant topological features through iterative refinement of atomic representations. It resides in the 'Enhanced Sampling and Conformational Exploration Techniques' leaf, which contains four papers including the original work. This leaf sits within 'Methodological Advances in MD Simulation Stability', a moderately populated branch focused on algorithmic innovations for stability and sampling efficiency. The leaf's scope emphasizes efficient conformational space exploration, distinguishing it from equilibrium MD applications, suggesting a specialized but not overcrowded research direction.

The taxonomy reveals neighboring methodological leaves addressing structural identification and stability validation, while application-focused branches explore protein dynamics, biomolecular interactions, and nucleic acid conformations. The original paper's leaf neighbors include Targeted Molecular Dynamics and Large Domain Motions, which tackle conformational exploration through physics-based or structure-guided approaches. GGND diverges by employing geometric deep learning to maintain equivariance and enable instantaneous information flow between atomic pairs, positioning it at the intersection of enhanced sampling and neural network-based force field prediction rather than traditional sampling acceleration techniques.

Among 24 candidates examined across three contributions, none yielded clear refutations. The GGND framework examined 10 candidates with zero refutable overlaps, the theoretical regret bound under geometric topological shifts examined 4 candidates with zero refutations, and the plug-and-play module integration examined 10 candidates also with zero refutations. This suggests that within the limited search scope, the specific combination of diffusion-based iterative refinement, geometric invariance guarantees, and plug-and-play modularity for existing equivariant networks appears relatively unexplored. However, the modest candidate pool means the analysis captures top semantic matches rather than exhaustive prior work coverage.

Based on the top-24 semantic matches and taxonomy structure, the work appears to occupy a distinct methodological niche combining geometric deep learning with MD stability concerns. The absence of refutable candidates within this limited scope indicates potential novelty, though the search does not encompass all possible related work in enhanced sampling, equivariant neural networks, or force field development. The taxonomy context suggests the paper bridges methodological innovation and practical simulation stability, a less densely populated intersection compared to purely application-driven protein dynamics studies.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
24
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Stable molecular dynamics simulation under conformational variations. The field addresses how biomolecules maintain or transition between structural states during MD simulations, spanning methodological innovations and diverse biological systems. The taxonomy organizes work into several major branches: methodological advances that improve sampling and simulation stability (including enhanced sampling techniques like Targeted Molecular Dynamics[9] and tools for analyzing conformational ensembles); protein conformational dynamics examining stability under various perturbations (e.g., Calmodulin Population Shift[15], Insulin Conformational Changes[14]); biomolecular interactions focusing on binding-induced conformational shifts (VEGF Inhibitor Stability[3], Ligand Binding Stability[45]); nucleic acid dynamics exploring RNA and DNA structural transitions (RNA Conformational Chemical[32], G-Quadruplex Mechanical Unfolding[49]); viral protein studies tracking pathogen-related conformational changes (Hemagglutinin Acidic Conformational[1], Spike Protein Variants[23]); mechanotransduction investigating force-driven conformational responses (Integrin Conformational Mechanics[20]); peptide aggregation dynamics; and computational tool development including Enhanced Sampling Methods[18]. A particularly active area involves enhanced sampling and conformational exploration techniques, where researchers balance computational efficiency against the need to capture rare but biologically important transitions. Classical approaches like Targeted Molecular Dynamics[9] and newer methods such as Large Domain Motions[42] tackle the challenge of exploring vast conformational landscapes, while works like Stapled Coiled-Coil Stability[48] examine how chemical modifications stabilize specific states. Geometric Neural Diffusion[0] sits within this methodological branch, emphasizing stability during conformational exploration through geometric deep learning approaches. Compared to traditional enhanced sampling frameworks like Enhanced Sampling Methods[18] or structure-based techniques in Large Domain Motions[42], Geometric Neural Diffusion[0] appears to leverage neural architectures that respect molecular geometry, potentially offering a data-driven complement to physics-based sampling strategies while maintaining simulation stability across diverse conformational states.

Claimed Contributions

Geometric Graph Neural Diffusion (GGND) framework

The authors introduce GGND, a framework that uses equivariant diffusion processes on fully-connected molecular graphs to learn features invariant to geometric topological shifts. This enables accurate force predictions for unseen molecular conformations and stable molecular dynamics simulations.

10 retrieved papers
Theoretical regret bound under geometric topological shifts

The authors establish formal theoretical guarantees showing that GGND controls representation changes at arbitrary polynomial rates relative to topological shifts. This regret bound ensures improved extrapolation to unseen conformations and enhanced simulation stability.

4 retrieved papers
Plug-and-play module for existing equivariant networks

GGND is designed as a modular component that integrates with existing local equivariant message-passing neural networks to enhance their out-of-domain performance while preserving in-domain accuracy, without requiring architectural redesign.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Geometric Graph Neural Diffusion (GGND) framework

The authors introduce GGND, a framework that uses equivariant diffusion processes on fully-connected molecular graphs to learn features invariant to geometric topological shifts. This enables accurate force predictions for unseen molecular conformations and stable molecular dynamics simulations.

Contribution

Theoretical regret bound under geometric topological shifts

The authors establish formal theoretical guarantees showing that GGND controls representation changes at arbitrary polynomial rates relative to topological shifts. This regret bound ensures improved extrapolation to unseen conformations and enhanced simulation stability.

Contribution

Plug-and-play module for existing equivariant networks

GGND is designed as a modular component that integrates with existing local equivariant message-passing neural networks to enhance their out-of-domain performance while preserving in-domain accuracy, without requiring architectural redesign.

Table of Contents