Aligning Protein Conformation Ensemble Generation with Physical Feedback

2025-05-30

AI Summary

This work addresses the lack of physical interpretability and energetic plausibility in data-driven protein conformation generation models. To this end, we propose Energy-Based Alignment (EBA), a novel paradigm that enables efficient, gradient-free co-calibration between generative models and molecular mechanics energy evaluation—eliminating reliance on unstable energy gradients. Methodologically, EBA integrates denoising diffusion models with a dynamic-weighted energy alignment mechanism, implicitly embedding physics-based constraints during sampling. On MD-based benchmarks, EBA achieves state-of-the-art performance, substantially improving the physical validity, thermodynamic consistency, and functional relevance of generated conformations. By bridging the gap between data-driven learning and first-principles energetics, EBA overcomes the “physics-agnostic” limitation inherent in purely statistical models. This work establishes a new, interpretable, and energetically calibratable paradigm for structural biology modeling and conformation-aware drug discovery.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.

Problem

• Align generative models with physical feedback for proteins
• Balance conformational states using energy differences
• Improve physical plausibility of protein ensemble generation

Innovation

• Energy-based Alignment (EBA) integrates physical feedback
• EBA balances conformational states using energy differences
• EBA improves physical plausibility of protein ensembles