ArticleNature communications2020
Learning molecular dynamics with simple language model built upon long short-term memory neural network.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Directional information flow as a tool for analyzing protein allostery.bioRxiv : the preprint server for biology · 2026Article
- Automatic selection of the best neural architecture for time series forecasting.Nature communications · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- SPHT-LSTM: a novel industrial equipment condition prediction method.Scientific reports · 2026Article
- Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by Nano-GPT.Journal of chemical theory and computation · 2025Article
- Adversarial training for dynamics matching in coarse-grained models.The Journal of chemical physics · 2025Article
- Nearl: extracting dynamic features from molecular dynamics trajectories for machine learning tasks.Bioinformatics (Oxford, England) · 2025Article
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control.Scientific reports · 2025Article
- Article
- Learning stochastic dynamics and predicting emergent behavior using transformers.Nature communications · 2024Article
- GPCR molecular dynamics forecasting using recurrent neural networks.Scientific reports · 2023Article
- Can language models be used for real-world urban-delivery route optimization?Innovation (Cambridge (Mass.)) · 2023Article
- Path sampling of recurrent neural networks by incorporating known physics.Nature communications · 2022Article
- Committor-Consistent Variational String Method.The journal of physical chemistry letters · 2022Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
- Collective variable discovery in the age of machine learning: reality, hype and everything in between.RSC advances · 2022Review
- Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface.Chemical reviews · 2022Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model's reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems.
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