ArticleNature methods2024
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 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
224 citing papers in PubMed.
- Article
- Biomolecular Interaction Prediction in the Pre- and Post-AlphaFold Era: The 8th CAPRI Evaluation.Proteins · 2026Article
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Article
- Evaluation of methods for AlphaFold-based integrative modeling.bioRxiv : the preprint server for biology · 2026Article
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- A curated benchmark for cofolding models on kinase conformational states.npj drug discovery · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.Nature methods · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- The prevalence of protein misfolding as a mechanism for hereditary deafness.PLoS genetics · 2026Article
- Similarity-Enhanced Representation Learning of Non-Canonical Amino Acids for Therapeutic Peptide Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Differential lipid selectivity of StARD phospholipid transporters revealed by native MS.Nature communications · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
164 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
34 authors.
Funding
Abstract
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
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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.