Paper List
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The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
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Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
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Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
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。