ArticleChemical reviews2021
Unsupervised Learning Methods for Molecular Simulation Data.
Article in Chemical reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 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
115 citing papers in PubMed.
- Article
- Comparative Analysis of Algorithms for Extracting Frames from Unexplored Regions in Molecular Dynamics Simulations.ACS omega · 2026Article
- Immune-activating PTL/nanowire composite coating on Ti surface enables macrophage-driven bacterial clearance and osseointegration.Bioactive materials · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- Learning Latent Representations to Bridge Coarse-Grained and Atomistic Resolutions in Polymer Simulations.Journal of chemical theory and computation · 2026Article
- Navigating the Solubility Landscape of APIs in Deep Eutectic Solvents: A Data-Driven Thermodynamic Taxonomy of Solvation Regimes and Mechanisms.Molecules (Basel, Switzerland) · 2026Article
- Lost in Projection? Gaussian Filtering Recovers Hidden Conformational States.The journal of physical chemistry letters · 2026Article
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Reading between the Chains: Surface Mapping and Druggable Pockets on the Biological Assemblies of DENV-2's Protein E.ACS omega · 2026Article
- Random Functions as Data Compressors for Machine Learning of Molecular Processes.Journal of chemical theory and computation · 2026Article
- Repurposing of natural products for spinocerebellar ataxia type 3 using integrated network pharmacology and in silico approaches.Scientific reports · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Enhanced Sampling and Free Energy Calculations in Protein Simulations.Advances in experimental medicine and biology · 2026Review
- Molecular dynamics as a tool for unveiling protein-like folding behavior in urethane-based macromolecules: the effect of chain length on the secondary structure of oligourethanes.Polymer chemistry · 2025Article
- The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication.Advanced functional materials · 2025Article
- Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss
Identifiers
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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.