标题： 基于扩散先验的自适应多模态蛋白质即插即用方法 显示英文标题

标题： Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors

摘要： 在反问题中，目标是恢复一个未知参数（例如，图像），该参数通常在测量过程中经历了某种有损或噪声的变换。 最近，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，将来自多个来源的噪声实验数据整合以指导这些模型仍然是一个重大挑战。 现有方法通常需要精确了解实验噪声水平，并为每种数据模态手动调整权重。 在本工作中，我们引入了Adam-PnP，这是一种插件式框架，利用来自多个异构实验源的梯度来引导预训练的蛋白质扩散模型。 我们的框架具有自适应噪声估计方案和动态模态加权机制，集成到扩散过程中，减少了对人工超参数调优的需求。 在复杂重建任务上的实验表明，使用Adam-PnP显著提高了准确性。 显示英文摘要

摘要： 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.