ArticleProceedings of the National Academy of Sciences of the United States of America2022
From data to noise to data for mixing physics across temperatures with generative artificial intelligence.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 47 citations in OpenAlex.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Adaptive Tensor Train Metadynamics for High-Dimensional Free Energy Exploration.Journal of chemical theory and computation · 2026Article
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Non-equilibrium active noise enhances generative memory in diffusion models.Research square · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Simulated Solute Tempering 2: An Efficient and Practical Approach to Protein Conformational Sampling and Binding Events.Journal of chemical theory and computation · 2025Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Inferring phase transitions and critical exponents from limited observations with thermodynamic maps.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.eLife · 2024Article
- Assessing the Accuracy and Efficiency of Free Energy Differences Obtained fromJournal of chemical theory and computation · 2024Article
- Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.ArXiv · 2024Article
- Modeling prebiotic chemistries with quantum accuracy at classical costs.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model.Journal of chemical theory and computation · 2024Article
- Machine-learning-based methods to generate conformational ensembles of disordered proteins.Biophysical journal · 2024Article
- Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling.Journal of chemical theory and computation · 2023Article
- Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network.The journal of physical chemistry letters · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on statistical mechanics and generative artificial intelligence that allows solving this problem. Specifically, we work with denoising diffusion probabilistic models and show how these models in combination with replica exchange molecular dynamics achieve superior sampling of the biomolecular energy landscape at temperatures that were never simulated without assuming any particular slow degrees of freedom. The key idea is to treat the temperature as a fluctuating random variable and not a control parameter as is usually done. This allows us to directly sample from the joint probability distribution in configuration and temperature space. The results here are demonstrated for a chirally symmetric peptide and single-strand RNA undergoing conformational transitions in all-atom water. We demonstrate how we can discover transition states and metastable states that were previously unseen at the temperature of interest and even bypass the need to perform further simulations for a wide range of temperatures. At the same time, any unphysical states are easily identifiable through very low Boltzmann weights. The procedure while shown here for a class of molecular simulations should be more generally applicable to mixing information across simulations and experiments with varying control parameters.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.