Paper List
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Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition
This paper addresses the fundamental question of how microscopic stochastic advantages in spatial exploration translate into macroscopic resource domi...
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Linear Readout of Neural Manifolds with Continuous Variables
This paper addresses the core challenge of quantifying how the geometric structure of high-dimensional neural population activity (neural manifolds) d...
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Theory of Cell Body Lensing and Phototaxis Sign Reversal in “Eyeless” Mutants of Chlamydomonas
This paper solves the core puzzle of how eyeless mutants of Chlamydomonas exhibit reversed phototaxis by quantitatively modeling the competition betwe...
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Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons
This paper addresses the challenge of predicting transcriptomic identity from electrophysiological recordings in human cortical interneurons, where li...
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Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
This paper addresses the core challenge of modeling large-scale neural population activity (1500-2000 neurons) with interpretable higher-order interac...
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Realizing Common Random Numbers: Event-Keyed Hashing for Causally Valid Stochastic Models
This paper addresses the critical problem that standard stateful PRNG implementations in agent-based models violate causal validity by making random d...
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A Standardized Framework for Evaluating Gene Expression Generative Models
This paper addresses the critical lack of standardized evaluation protocols for single-cell gene expression generative models, where inconsistent metr...
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Single Molecule Localization Microscopy Challenge: A Biologically Inspired Benchmark for Long-Sequence Modeling
This paper addresses the core challenge of evaluating state-space models on biologically realistic, sparse, and stochastic temporal processes, which a...
基于状态空间模型的生物分子动力学原子轨迹建模
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IN SHORT: ATMOS通过提供一个基于SSM的高效框架，用于生物分子的原子级轨迹生成，弥合了计算昂贵的MD模拟与时间受限的深度生成模型之间的差距。
核心创新
- Methodology First unified generative framework for simulating dynamics of biomolecules, generalizing from monomeric proteins to complex biomolecular systems.
- Methodology Models dynamics at the fully atomic level by operating directly on atomic coordinates during both encoding and decoding phases, preserving detailed geometric contexts.
- Methodology Innovatively adapts State Space Models to trajectory generation, enabling capture of long-range temporal dependencies with linear computational complexity.
主要结论
- ATMOS在大规模MD数据集（mdCATH和MISATO）上，在生成蛋白质单体和复杂蛋白质-配体系统的构象轨迹方面达到了最先进的性能。
- 该框架在推断过程中每步具有线性计算复杂度O(1)，与标准注意力机制的二次复杂度相比，能够高效生成长时间范围的轨迹。
- 通过将静态结构先验与动态轨迹生成相结合，ATMOS将静态结构预测扩展到动力学领域，为动力学基础模型铺平了道路。
摘要: 理解生物分子的动态行为对于阐明生物功能和促进药物发现至关重要。虽然分子动力学（MD）模拟为研究这些动力学提供了严格的物理基础，但对于长时间尺度而言，其计算成本仍然很高。相反，最近的深度生成模型加速了构象生成，但通常要么无法建模时间关系，要么仅针对单体蛋白质构建。为了弥合这一差距，我们引入了ATMOS，一种基于状态空间模型（SSM）的新型生成框架，旨在为生物分子系统生成原子级的MD轨迹。ATMOS集成了基于Pairformer的状态转移机制以捕获长程时间依赖性，以及一个基于扩散的模块以自回归方式解码轨迹帧。ATMOS在PDB的晶体结构和大规模MD模拟数据集（包括mdCATH和MISATO）的构象轨迹上进行训练。我们证明ATMOS在生成蛋白质单体和复杂蛋白质-配体系统的构象轨迹方面达到了最先进的性能。通过实现对原子运动轨迹的高效推断，这项工作为建模生物分子动力学奠定了有前景的基础。