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Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics

arXiv:2604.03911v1 Announce Type: new Abstract: Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pretraining for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 a

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Aniketh Iyengar, Jiaqi Han, Pengwei Sun, Mingjian Jiang, Jianwen Xie, Stefano Ermon · · 1 min read · 13 views
#cs.LG #q-bio.QM

arXiv:2604.03911v1 Announce Type: new Abstract: Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pretraining for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 and DRUGS small-molecule datasets across unconditional generation, forward simulation, and interpolation tasks, and further extend our framework and analysis to tetrapeptide and protein monomer systems. Experimental results confirm that our approach excels in generating chemically realistic MD trajectories, as evidenced by remarkable improvements of accuracy in geometric, dynamical, and energetic measurements.

Executive Summary

This article proposes a novel framework for generating molecular dynamics (MD) trajectories using deep generative models. The framework, which leverages structure pretraining, effectively mitigates the scarcity of MD trajectory data by decomposing the task into two manageable subproblems: structural generation and temporal alignment. The authors comprehensively evaluate their method on various small-molecule datasets and demonstrate remarkable improvements in accuracy across geometric, dynamical, and energetic measurements. While the article presents a promising approach, its limitations and potential applications warrant further exploration. The proposed framework has the potential to significantly advance the field of MD trajectory generation, with practical implications for drug discovery and protein engineering.

Key Points

  • ▸ Structure pretraining is leveraged to mitigate the scarcity of MD trajectory data.
  • ▸ The framework decomposes the MD task into two manageable subproblems: structural generation and temporal alignment.
  • ▸ Comprehensive evaluation demonstrates significant improvements in accuracy across various metrics.

Merits

Strength in generative capabilities

The proposed framework exhibits exceptional generative capabilities, effectively capturing the complex dynamics of molecular systems.

Effective decomposition of the MD task

The framework's ability to decompose the MD task into two manageable subproblems enables more efficient and effective trajectory generation.

Demerits

Limited generalizability to larger systems

The framework's performance on larger systems, such as proteins and peptides, remains unclear and warrants further investigation.

Potential overreliance on pretraining data

The framework's reliance on pretraining data may limit its applicability to systems with limited structural data.

Expert Commentary

The proposed framework presents a promising approach to MD trajectory generation, leveraging structure pretraining to effectively mitigate the scarcity of MD trajectory data. While the article demonstrates significant improvements in accuracy across various metrics, further investigation is warranted to address the limitations of the framework, particularly with regard to its generalizability to larger systems. The article's implications for drug discovery and protein engineering are substantial, and the framework's potential to accelerate these fields is significant. As the field of deep learning continues to evolve, it is essential to prioritize research on developing more robust and generalizable frameworks for chemistry applications.

Recommendations

  • ✓ Further investigation is needed to evaluate the framework's performance on larger systems and assess its potential for real-world applications.
  • ✓ The development of more robust and generalizable frameworks for MD trajectory generation should be a priority for future research in chemistry applications.

Sources

Original: arXiv - cs.LG

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