Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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。