# Energy-based models for atomic-resolution protein conformations

arXiv: https://arxiv.org/abs/2004.13167 · HTML: https://mlsanity.com/paper/2004.13167/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2205.15019/) (2022)
- [A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model](https://mlsanity.com/paper/2205.06924/) (2022)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [Guiding Energy-based Models via Contrastive Latent Variables](https://mlsanity.com/paper/2303.03023/) (2023)
- [Deep Generative Modeling for Protein Design](https://mlsanity.com/paper/2109.13754/) (2021)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models](https://mlsanity.com/paper/2406.07008/) (2024)
- [Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models](https://mlsanity.com/paper/2210.05782/) (2022)
- [Energy-Based Models for Functional Data using Path Measure Tilting](https://mlsanity.com/paper/2202.01929/) (2022)
- [Graph Energy-based Model for Substructure Preserving Molecular Design](https://mlsanity.com/paper/2102.04600/) (2021)
- [EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models](https://mlsanity.com/paper/2105.04771/) (2021)
- [Versatile Energy-Based Probabilistic Models for High Energy Physics](https://mlsanity.com/paper/2302.00695/) (2023)
- [Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection](https://mlsanity.com/paper/2505.21285/) (2025)
- [NeuroGame Transformer: Gibbs-Inspired Attention Driven by Game Theory and Statistical Physics](https://mlsanity.com/paper/2603.18761/) (2026)
- [Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data](https://mlsanity.com/paper/2108.04227/) (2021)
- [ProtGO: A Transformer based Fusion Model for accurately predicting Gene Ontology (GO) Terms from full scale Protein Sequences](https://mlsanity.com/paper/2412.05776/) (2024)
- [ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity](https://mlsanity.com/paper/2502.02026/) (2025)
- [RNA Alternative Splicing Prediction with Discrete Compositional Energy Network](https://mlsanity.com/paper/2103.04246/) (2021)
- [Adversarial Stein Training for Graph Energy Models](https://mlsanity.com/paper/2108.12982/) (2021)
- [STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models](https://mlsanity.com/paper/2310.12667/) (2023)

## Abstract (arXiv)

We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex product of several decades of research and tuning. To evaluate the model, we benchmark on the rotamer recovery task, the problem of predicting the conformation of a side chain from its context within a protein structure, which has been used to evaluate energy functions for protein design. The model achieves performance close to that of the Rosetta energy function, a state-of-the-art method widely used in protein structure prediction and design. An investigation of the model's outputs and hidden representations finds that it captures physicochemical properties relevant to protein energy.