ArticlePloS one2021
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters.npj computational materials · 2026Article
- Reversible molecular simulation for training classical and machine-learning force fields.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Importance of Secondary Structure Data in Large Scale Protein Modeling Using Low-Resolution SURPASS Method.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics.Journal of chemical information and modeling · 2024Article
- Differentiable simulation to develop molecular dynamics force fields for disordered proteins.Chemical science · 2024Article
- Top-Down Machine Learning of Coarse-Grained Protein Force Fields.Journal of chemical theory and computation · 2023Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
- Deep learning approaches for conformational flexibility and switching properties in protein design.Frontiers in molecular biosciences · 2022Review
- TorchMD: A Deep Learning Framework for Molecular Simulations.Journal of chemical theory and computation · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.
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.