基于可迁移归一化流的 amortized 模拟
Amortized Sampling with Transferable Normalizing Flows
AI总结：
本文提出 Prose，一个基于深度学习的可迁移归一化流，用于高效跨系统分子采样，通过零样本提案实现序列长度的可转移性，并在多种采样算法中表现出竞争力。
摘要：
分子构象的高效平衡采样仍然是计算化学和统计推断中的核心挑战。经典方法如分子动力学或马尔可夫链蒙特卡洛方法本质上缺乏 amortization；采样计算成本必须为每个感兴趣的系统完全支付。生成模型的广泛应用激发了通过学习采样算法克服这一限制的兴趣。尽管在单个系统上训练时表现与传统方法相当，但学习采样器迄今为止在跨系统转移方面显示出有限的能力。我们证明深度学习能够通过引入 Prose，一个具有2.85亿参数的全原子可迁移归一化流，设计出可扩展且可转移的采样器。Prose 在任意肽系统上生成零样本无相关提案样本，实现了在序列长度上的先前不可行的可转移性，同时保持归一化流的高效似然评估。通过广泛的实证评估，我们证明 Prose 作为多种采样算法提案的有效性，发现通过简单的基于重要性采样的微调过程，可以实现与已建立方法如顺序蒙特卡洛竞争的性能。我们开源了 Prose 的代码库、模型权重和训练数据集，以进一步促进对 amortized 采样方法和目标的研究。
英文摘要：
Efficient equilibrium sampling of molecular conformations remains a core challenge in computational chemistry and statistical inference. Classical approaches such as molecular dynamics or Markov chain Monte Carlo inherently lack amortization; the computational cost of sampling must be paid in full for each system of interest. The widespread success of generative models has inspired interest towards overcoming this limitation through learning sampling algorithms. Despite performing competitively with conventional methods when trained on a single system, learned samplers have so far demonstrated limited ability to transfer across systems. We demonstrate that deep learning enables the design of scalable and transferable samplers by introducing Prose, a 285 million parameter all-atom transferable normalizing flow trained on a corpus of peptide molecular dynamics trajectories up to 8 residues in length. Prose draws zero-shot uncorrelated proposal samples for arbitrary peptide systems, achieving the previously intractable transferability across sequence length, whilst retaining the efficient likelihood evaluation of normalizing flows. Through extensive empirical evaluation we demonstrate the efficacy of Prose as a proposal for a variety of sampling algorithms, finding a simple importance sampling-based fine-tuning procedure to achieve competitive performance to established methods such as sequential Monte Carlo. We open-source the Prose codebase, model weights, and training dataset, to further stimulate research into amortized sampling methods and objectives.
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