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

TL;DR

This paper presents Adam-PnP, a novel adaptive framework that effectively integrates multiple noisy experimental data sources to guide diffusion-based protein structure generation, reducing manual tuning and improving reconstruction accuracy.

Contribution

The work introduces an adaptive, plug-and-play method for guiding pre-trained diffusion models with multiple data modalities, eliminating the need for manual hyperparameter tuning.

Findings

Significantly improved reconstruction accuracy on complex tasks.

Effective handling of heterogeneous and noisy data sources.

Reduced manual hyperparameter tuning in protein structure generation.

Abstract

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.