用自适应噪声估计和动态加权，让蛋白质生成模型更准地融合多种实验数据。
Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
- 通过扩散模型与多源实验数据联合优化，实现蛋白质结构重建。
- 在复杂重构任务中，相比传统方法提升显著，无需手动调参。
- 适合需要高精度蛋白结构建模的研究者，尤其擅长处理不完整数据。
逆问题的目标是恢复因测量过程中的损失或噪声而退化的未知参数（如蛋白质结构）。近年来，深度生成模型尤其是扩散模型已成为蛋白质结构生成的强大先验。然而，如何将来自多个异构来源的噪声实验数据有效整合以指导生成模型仍是一大挑战。现有方法通常依赖对实验噪声水平的精确知识，并需为每种数据模态手动调整权重。本文提出Adam-PnP，一种插件式框架，利用多源异构实验数据的梯度引导预训练的蛋白质扩散模型。该框架集成了自适应噪声估计与动态模态加权机制，嵌入扩散过程，大幅减少对人工超参数调优的需求。在复杂重构任务上的实验表明，Adam-PnP显著提升了生成准确性。
原文摘要 · Abstract (English)
In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, deep generative models, particularly diffusion models, have emerged as powerful priors for protein structure generation. However, integrating noisy experimental data from multiple sources to guide these models remains a significant challenge. Existing methods often require precise knowledge of experimental noise levels and manually tuned weights for each data modality. In this work, we introduce Adam-PnP, a Plug-and-Play framework that guides a pre-trained protein diffusion model using gradients from multiple, heterogeneous experimental sources. Our framework features an adaptive noise estimation scheme and a dynamic modality weighting mechanism integrated into the diffusion process, which reduce the need for manual hyperparameter tuning. Experiments on complex reconstruction tasks demonstrate significantly improved accuracy using Adam-PnP.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。