ArticleThe journal of physical chemistry. B2025
Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules.
Article in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Interpretable machine learning uncovers structural determinants of Wnt-Wntless binding specificity from atomistic simulations.Communications chemistry · 2026Article
- Identification of Key Residues in Allosteric Signaling of Photoactivated Adenylyl Cyclase.bioRxiv : the preprint server for biology · 2026Article
- Special Issue: "Advanced Research on Molecular Modeling of Protein Structure and Functions".International journal of molecular sciences · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) techniques have become powerful tools in both industrial and academic settings. Their ability to facilitate analysis of complex data and generation of predictive insights is transforming how scientific problems are approached across a wide range of disciplines. In this tutorial, we present a cursory introduction to three widely used ML techniques─logistic regression, random forest, and multilayer perceptron─applied toward analyzing molecular dynamics (MD) trajectory data. We employ our chosen ML models to the study of the SARS-CoV-2 spike protein receptor binding domain interacting with the receptor ACE2. We develop a pipeline for processing MD simulation trajectory data and identifying residues that significantly impact the stability of the complex.
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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.