ArticleThe European physical journal. E, Soft matter2021
AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations.
Article in The European physical journal. E, Soft matter, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Interpretable machine learning uncovers structural determinants of Wnt-Wntless binding specificity from atomistic simulations.Communications chemistry · 2026Article
- Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly.Journal of chemical information and modeling · 2025Article
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 2025Review
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation.The journal of physical chemistry. B · 2024Article
- Structural bases for the higher adherence to ACE2 conferred by the SARS-CoV-2 spike Q498Y substitution.Acta crystallographica. Section D, Structural biology · 2022Article
- Modeling the Dynamics of Protein-Protein Interfaces, How and Why?Molecules (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.
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Registered trials
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