ReviewRSC advances2022
Collective variable discovery in the age of machine learning: reality, hype and everything in between.
Review in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed.
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Toward Accurate RNA Folding Thermodynamics: Evaluation of Enhanced Sampling Methods for Force Field Benchmarking.Journal of chemical theory and computation · 2026Article
- Advanced Computational Methods in Protein Simulations: A Case Study of Enhanced Sampling Applied to Membrane Transporters.Advances in experimental medicine and biology · 2026Review
- Coupling causality and interpretable machine learning to reveal the reaction coordinate of C-N coupling with a supramolecular Cu-calix[8]arene catalyst.Digital discovery · 2025Article
- Special Issue: "Advanced Research on Molecular Modeling of Protein Structure and Functions".International journal of molecular sciences · 2025Article
- Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.Journal of chemical theory and computation · 2025Article
- Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.Molecular pharmaceutics · 2025Review
- Running Gaussian-accelerated Molecular Dynamics Simulations in NAMD [Article v1.0].Living journal of computational molecular science · 2025Article
- Prediction of Threonine-Tyrosine Kinase Receptor-Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics.The journal of physical chemistry letters · 2024Article
- Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.International journal of molecular sciences · 2024Review
- Automated collective variable discovery for MFSD2A transporter from molecular dynamics simulations.Biophysical journal · 2024Article
- A Stochastic Landscape Approach for Protein Folding State Classification.Journal of chemical theory and computation · 2024Article
- Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.Journal of chemical theory and computation · 2024Article
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- Article
- How Robust Is the Ligand Binding Transition State?Journal of the American Chemical Society · 2023Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations.ACS catalysis · 2023Review
- Approximating conformational Boltzmann distributions with AlphaFold2 predictions.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.
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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.