ArticleNature computational science2024
MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
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Who cites it
29 citing papers in PubMed.
- Biomolecular Interaction Prediction in the Pre- and Post-AlphaFold Era: The 8th CAPRI Evaluation.Proteins · 2026Article
- Massive Sampling Strategy for Antibody-Antigen Targets in CAPRI Round 55 With MassiveFold.Proteins · 2026Article
- ABAG-Rank: improving model selection of AlphaFold antibody-antigen complexes by learning to rank.Bioinformatics (Oxford, England) · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Microproteins in Human Physiology and Pathology.Biochemistry · 2026Review
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes.Bioinformatics (Oxford, England) · 2026Article
- Efficient global accuracy estimation for protein complex structural models using multi-view representation learning.Cell reports methods · 2026Article
- DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.Chemical science · 2026Article
- Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context.Bioinformatics (Oxford, England) · 2026Article
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- Highlights of Model Quality Assessment in CASP16.Proteins · 2026Article
- CASP16 Protein Monomer Structure Prediction Assessment.Proteins · 2026Article
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- Model Quality Assessment for CASP16.Proteins · 2026Article
- Improving AlphaFold2- and AlphaFold3-Based Protein Complex Structure Prediction With MULTICOM4 in CASP16.Proteins · 2026Article
- Practical Outcomes From CASP16 for Users in Need of Biomolecular Structure Prediction.Proteins · 2026Review
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- SeaMoon: From protein language models to continuous structural heterogeneity.Structure (London, England : 1993) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.
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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.