ArticleJournal of chemical theory and computation2026
Generative Autoencoders Coupled to Monte Carlo Simulation Allow Efficient Protein Conformation Sampling.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular simulations of proteins are well-known to be computationally expensive. Here, we present a new latent-space-based method for modeling protein conformational flexibility at a very affordable computational cost. The method is data-driven and employs an autoencoder-based machine learning model for reversible dimensionality reduction of diverse conformations of the protein studied. Next, samples are selected from the low-dimensional latent space via Monte Carlo sampling. The folding and unfolding of the miniproteins can be sampled in minutes of computational time. We validated the method on four model systems: Tryptophan Cage, nonfolding variant of Tryptophan Cage, Villin headpiece, and human β-2-syntrophin PDZ domain (miniproteins with 20, 20, 35, and 95 residues, respectively). All systems were modeled at an all-atom resolution. Tryptophan Cage and Villin miniproteins show very similar populations of folded/unfolded states sampled by Monte Carlo simulations as the reference MD trajectories calculated by D. E. Shaw Research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.