ArticleFrontiers in molecular biosciences2021
A Deep Graph Network-Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics.ACS central science · 2023Article
- Bottom-up Coarse-Graining: Principles and Perspectives.Journal of chemical theory and computation · 2022Review
- From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules.Frontiers in molecular biosciences · 2021Review
- A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules.The European physical journal. B · 2021Article
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Authors and funding
6 authors.
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Abstract
The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless development of computer architectures and algorithms. The consequent explosion in the number and extent of MD trajectories induces the need for automated methods to rationalize the raw data and make quantitative sense of them. Recently, an algorithmic approach was introduced by some of us to identify the subset of a protein's atoms, or mapping, that enables the most informative description of the system. This method relies on the computation, for a given reduced representation, of the associated mapping entropy, that is, a measure of the information loss due to such simplification; albeit relatively straightforward, this calculation can be time-consuming. Here, we describe the implementation of a deep learning approach aimed at accelerating the calculation of the mapping entropy. We rely on Deep Graph Networks, which provide extreme flexibility in handling structured input data and whose predictions prove to be accurate and-remarkably efficient. The trained network produces a speedup factor as large as 10
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