# Machine learning for protein folding and dynamics

arXiv: https://arxiv.org/abs/1911.09811 · HTML: https://mlsanity.com/paper/1911.09811/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 15 of 15)

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- [Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction](https://mlsanity.com/paper/2211.14939/) (2022)
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- [Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning](https://mlsanity.com/paper/2205.03339/) (2022)
- [Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems](https://mlsanity.com/paper/2309.07936/) (2023)
- [Random functions as data compressors for machine learning of molecular processes](https://mlsanity.com/paper/2509.17937/) (2025)
- [Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference](https://mlsanity.com/paper/2510.07457/) (2025)
- [Energy Loss Functions for Physical Systems](https://mlsanity.com/paper/2511.02087/) (2025)
- [Towards unsupervised representation learning for quantum data: quantum models with inference and generation](https://mlsanity.com/paper/2609.00372/) (2026)

## Abstract (arXiv)

Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation.