ArticleChemical science2020
Molecular latent space simulators.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Generative Autoencoders Coupled to Monte Carlo Simulation Allow Efficient Protein Conformation Sampling.Journal of chemical theory and computation · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.The Journal of chemical physics · 2026Article
- Transferable generative models bridge femtosecond to nanosecond time-step molecular dynamics.Science advances · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.bioRxiv : the preprint server for biology · 2025Article
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Kinetically Consistent Coarse Graining Using Kernel-Based Extended Dynamic Mode Decomposition.Journal of chemical theory and computation · 2025Article
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Generating Protein Structures for Pathway Discovery Using Deep Learning.Journal of chemical theory and computation · 2024Article
- Data-driven prediction of αBiophysical journal · 2024Article
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution.The journal of physical chemistry. A · 2022Article
- Path sampling of recurrent neural networks by incorporating known physics.Nature communications · 2022Article
- Article
- Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.Chemical science · 2022Article
- Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders.Journal of chemical theory and computation · 2022Article
- Adaptive Monte Carlo augmented with normalizing flows.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.Journal of chemical information and modeling · 2022Article
- Learning molecular dynamics with simple language model built upon long short-term memory neural network.Nature communications · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous atomistic molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous atomistic simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce atomistic molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.
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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.