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OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
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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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“allow users to run a modified AF2 protocol on open access servers in minutes”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [9]
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Cites
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InfluentialHighly accurate protein structure prediction with AlphaFold
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Evolutionary-scale prediction of atomic level protein structure with a language model
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Boltz-1 Democratizing Biomolecular Interaction ModelingPreprint
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ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model
Huang, H., Zheng, O., Wang, D., Yin, J., Wang, Z., Ding, S., Yin, H., Xu, C., Yang, R., Zheng, Q., et al. (2023) · International Journal of Oral Science · doi:10.1038/s41368-023-00239-y · Cited by 324
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“Multiple sequence alignment (MSA) EvoDiff models were trained on OpenFold [31], which contains 401,381 MSAs for 140,000 unique Protein Data Bank (PDB) chains and 16,000,000 UniClust30 clusters.”
MethodEasy and accurate protein structure prediction using ColabFold
Kim, G., Lee, S., Karin, E. L., Kim, H., Moriwaki, Y., Ovchinnikov, S., Steinegger, M., & Mirdita, M. (2024) · Nature Protocols · doi:10.1038/s41596-024-01060-5 · Cited by 170
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Stahl, K., Graziadei, A., Dau, T., Brock, O., & Rappsilber, J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01704-z · Cited by 172
“We use OpenFold v0.”
InfluentialAlphaFold predictions of fold-switched conformations are driven by structure memorization
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159
“…contacts medium gray; coevolutionary information inferred from MSA using ACE, (teal). c Both AF2 multimer and AF2 predict the correctXCL1 dimerstructurefrom single sequences and 0 recycles, suggesting that they memorized its structure during training. match AF2 ’ s predictive performance 27 .”
InfluentialState-specific protein–ligand complex structure prediction with a multiscale deep generative model
Qiao, Z., Nie, W., Vahdat, A., Miller, T. F., & Anandkumar, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00792-z · Cited by 210
Efficient and accurate prediction of protein structure using RoseTTAFold2Preprint
Baek, M., Anishchenko, I. V., Humphreys, I. R., Cong, Q., Baker, D. A., & DiMaio, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.24.542179 · Cited by 170
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155
Yin, R., & Pierce, B. G. (2024) · Protein Science · doi:10.1002/pro.4865 · Cited by 145
AI-Driven Deep Learning Techniques in Protein Structure Prediction
Chen, L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z., Chen, J., & Xie, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25158426 · Cited by 152
“OpenComplex is based on AlphaFold2 [25] and OpenFold [108].”
MethodProtenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 ReproductionPreprint
ByteDance AML AI4Science Team, Chen, X., Zhang, Y., Lu, C., Ma, W., Guan, J., Gong, C., Yang, J., Zhang, H., Zhang, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631967 · Cited by 167
Structure prediction of alternative protein conformations
Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105
AlphaFold prediction of structural ensembles of disordered proteins
Brotzakis, Z. F., Zhang, S., Murtada, M. H., & Vendruscolo, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56572-9 · Cited by 99
Self-citationAFsample2 predicts multiple conformations and ensembles with AlphaFold2
Kalakoti, Y., & Wallner, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07791-9 · Cited by 126
“Theideaisnotlimitedto theAF2inferencesystembutcouldbereadilyappliedtootherMSA-based predictionsystems,suchasRosettaFold 9 , OpenFold 25 , or AF3 26 .”
Progress at protein structure prediction, as seen in CASP15
Elofsson, A. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102594 · Cited by 97
Qiu, X., Li, H., Steeg, G. V., & Godzik, A. (2024) · Biomolecules · doi:10.3390/biom14030339 · Cited by 103
“The AF2 breakthrough has been quickly followed by other AI tools such as RoseTTAfold [7], ESMFold [8], and OpenFold [9].”
BackgroundMethodKoh, H. Y., Zheng, Y., Yang, M., Arora, R., Webb, G. I., Pan, S., Li, L., & Church, G. M. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00349-8 · Cited by 117
Bartolec, T. K., Vázquez-Campos, X., Norman, A. R., Luong, C., Johnson, M., Payne, R. J., Wilkins, M. R., Mackay, J. P., & Low, J. K. K. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2219418120 · Cited by 83
“Therefore, development of more computationally efficient structural prediction software [e.g., OpenFold (79)], will be required to enable the prediction of missing subsets of the structural proteome and interactome.”
Deep language models for interpretative and predictive materials science
Hu, Y., & Buehler, M. J. (2023) · APL Machine Learning · doi:10.1063/5.0134317 · Cited by 85
Cai, H., Shen, C., Jian, T., Zhang, X., Chen, T., Han, X., Yang, Z., Dang, W., Hsieh, C., Kang, Y., et al. (2023) · Chemical Science · doi:10.1039/d3sc05552c · Cited by 79
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 76
Geometric deep learning of protein–DNA binding specificity
Mitra, R., Li, J., Sagendorf, J. M., Jiang, Y., Cohen, A. S., Chiu, T.-P., Glasscock, C. J., & Rohs, R. (2024) · Nature Methods · doi:10.1038/s41592-024-02372-w · Cited by 75
“The rapid advancement of protein structure prediction meth-ods, including AlphaFold 26 , OpenFold 27 and RoseTTAFold 28 , along with protein–DNA complex modelers, such as RoseTTAFoldNA (RFNA) 29 , RoseTTAFold All-Atom 30 , MELD-DNA 31 and AlphaFold3 (ref.”
MethodNovel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
“DeepMind ’ s AF2 outperformed others in CASP14 [26], and reports suggest that high-quality models can be comparable to crystallographic structures, with competing methods reproducing DeepMind ’ s results [24,27,28].”
ResultDeep learning for protein structure prediction and design—progress and applications
Jänes, J., & Beltrão, P. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00016-x · Cited by 76
“Finally, OpenFold (Ahdritz et al, 2022) is a reimplementation of AlphaFold2 that has the same architecture but having the code available as well as the data required for re-training.”
BackgroundMethodInfluentialTheoretical and Data-Driven Approaches for Biomolecular Condensates
Saar, K. L., Qian, D., Good, L. L., Morgunov, A. S., Collepardo‐Guevara, R., Best, R. B., & Knowles, T. P. J. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00586 · Cited by 66
Casadevall, G., Duran, C., & Osuna, S. (2023) · JACS Au · doi:10.1021/jacsau.3c00188 · Cited by 70
FoldScript: a web server for the efficient analysis of AI-generated 3D protein models
Robert, X., Guillon, C., & Gouet, P. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf326 · Cited by 70
“This server allows to quickly and automatically analyse a set of models generated by AF3 and other AI-driven predictors (AF2-multimer, RoseTTAFold, ESMfold [ 7 ], OpenFold [ 8 ], etc.).”
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Kleiman, D. E., Nadeem, H., & Shukla, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04843 · Cited by 70
Transferable deep generative modeling of intrinsically disordered protein conformations
Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62
“This is in line with what has been observed for the OpenFold model [46]: protein modeling accuracy increases rapidly with the number of training systems and then continues to slowly improve by providing more systems.”
ResultDiaz, D. J., Gong, C., Ouyang-Zhang, J., Loy, J. M., Wells, J., Yang, D. K., Ellington, A. D., Dimakis, A. G., & Klivans, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-49780-2 · Cited by 69
“Deep learning is currently revolutionizing many physical and biological disciplines 17 – 21 , with AlphaFoldV2 leading the way as the scienti fi c breakthrough of the year in 2021 22,23 and sparking an entire waveofdeeplearningstructurepredictiontools 24,25 .”
Artificial intelligence in peptide-based drug design
Zhai, S., Liu, T., Lin, S., Li, D., Liu, H., Yao, X., & Hou, T. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104300 · Cited by 67
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions
Varga, J. K., Ovchinnikov, S., & Schueler‐Furman, O. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf107 · Cited by 56
“Since 2020, AlphaFold2 [AF2 (Evans et al. 2021, Jumper et al. 2021)] and other similar deep learning algorithms (Baek et al. 2023, Ahdritz et al. 2024) have revolutionized structural biology, by making swift and confident predictions of millions of protein structures and complexes available.”
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention
Lau, A. M., Kandathil, S. M., & Jones, D. T. (2023) · Nature Communications · doi:10.1038/s41467-023-43934-4 · Cited by 48
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 56
Large language models for drug discovery and development
Zheng, Y., Koh, H. Y., Ju, J., Yang, M., May, L. T., Webb, G. I., Li, L., Pan, S., & Church, G. M. (2025) · Patterns · doi:10.1016/j.patter.2025.101346 · Cited by 121
Strengthening nucleic acid biosecurity screening against generative protein design tools
Wittmann, B. J., Alexanian, T., Bartling, C. M., Beal, J., Clore, A. J., Diggans, J., Flyangolts, K., Gemler, B. T., Mitchell, T., Murphy, S. T., et al. (2025) · Science · doi:10.1126/science.adu8578 · Cited by 86
Are protein language models the new universal key?
Weißenow, K., & Rost, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102997 · Cited by 54
Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes
Mirabello, C., Wallner, B., Nystedt, B., Azinas, S., & Carroni, M. (2024) · Nature Communications · doi:10.1038/s41467-024-52951-w · Cited by 53
“Other integrative approaches propose retraining versions of AlphaFold that are better at taking experiments into account: versions of OpenFold 12 and Uni-Fold 13 (retrainable implementations of Alpha-Fold)acceptextrainputdataderivedfromcrosslinkingexperimentsto guide monomeric and multimeric…”
MethodAlphaBridge: Tools for the analysis of predicted biomolecular complexes
Salmoral, D. Á., Borza, R., Maiella, C., Kwee, B. P. Y., Xie, R., Joosten, R. P., Hekkelman, M. L., & Anastassis Perrakis, A. (2026) · Structure · doi:10.1016/j.str.2026.08.011 · Cited by 50
An end-to-end framework for the prediction of protein structure and fitness from single sequence
Chen, Y., Xu, Y., Liu, D., Xing, Y., & Gong, H. (2024) · Nature Communications · doi:10.1038/s41467-024-51776-x · Cited by 45
Protein–protein interaction prediction methods: from docking-based to AI-based approaches
Tsuchiya, Y., Yamamori, Y., & Tomii, K. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-01032-7 · Cited by 46
“Among the follow-up replicative studies of AF2, such as OpenFold (Ahdritz et al. 2022), Uni-Fold (Li et al. 2022a, b), MegaFold (Liu et al. 2022), and HelixFold (Wang et al. 2022), it was only Uni-Fold and Uni-Fold-symmetry (Li et al. 2022b) that could succeed not only in monomer prediction but also in multimer predic…”
MethodInfluentialEnd-to-end protein–ligand complex structure generation with diffusion-based generative models
Nakata, S., Mori, Y., & Tanaka, S. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05354-5 · Cited by 45
“These methods are regression-based and rely on ad hoc techniques like MSA subsampling, changing the number of recycles, and enabling dropout during inference to sample multiple structures [44].”
BackgroundMethodBrown, B. P., Stein, R. A., Meiler, J., & Mchaourab, H. S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01081 · Cited by 41
Liu, J., Wu, L., Xie, A., Liu, W. L., Zhao, H., Wan, Y., & Mao, W. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03169-5 · Cited by 42
“Protein and antibody structure prediction has been revolutionized by deep learning-based tools such as AlphaFold2 [178], RoseTTAFold [209], OmegaFold [210], ESMFold [211], OpenFold [212], IgFold [213] and ABodyBuilder3 [214].”
Protein representations: Encoding biological information for machine learning in biocatalysis
Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38
Jin, S., Zeng, Z., Xiong, X., Huang, B., Tang, L., Wang, H., Ma, X., Tang, X., Shao, G., Huang, X., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08282-7 · Cited by 43
ABodyBuilder3: improved and scalable antibody structure predictions
Kenlay, H., Dreyer, F. A., Cutting, D., Nissley, D. A., & Deane, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae576 · Cited by 44
“We develop an efficient and scalable implementation of the ABodyBuilder2 architecture which makes use of vectorization to improve hardware utilization, leveraging optimisations from the OpenFold project (Ahdritz et al. 2024).”
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
Aranganathan, A., Gu, X., Wang, D., Vani, B. P., & Tiwary, P. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103000 · Cited by 39
“From a data-based methods perspective, with the availability of an enormous structural database, particularly for proteins, methods such as AlphaFold2 (AF2) [7], RoseTTAFold2 [8], and OpenFold [9], have been developed to solve the protein folding problem.”
On the design space between molecular mechanics and machine learning force fields
Wang, Y., Takaba, K., Chen, M. S., Wieder, M., Xu, Y., Zhu, T., Zhang, J. Z. H., Nagle, A., Yu, K., Wang, X., et al. (2025) · Applied Physics Reviews · doi:10.1063/5.0237876 · Cited by 48
“It has quickly emerged to become a focus of cutting-edge research to generate samples directly on the coordinate space for biomolecular systems of interest— from small molecule conformer generation 272–276 , folded protein structure prediction 277–279 , to trajectory forecasting 30,280 .”
Representing structures of the multiple conformational states of proteins
Ramelot, T. A., Tejero, R., & Montelione, G. T. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102703 · Cited by 26
“In particular, as the machine learning (ML) methods of AlphaFold2 (AF2) [2], RosettaFold [3], OpenFold [4], ESMFold [5], RaptorX [6], and other advanced techniques have reached the stage where single structure prediction of small proteins is robust and reliable, and a current frontier is…”
MethodAlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles
Bowman, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-011435 · Cited by 42
Akpinaroglu, D., Seki, K., Guo, A., Zhu, E., Kelly, M. J. S., & Kortemme, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.15.571823 · Cited by 36
Natural Language Processing Methods for the Study of Protein–Ligand Interactions
Michels, J., Bandarupalli, R., Akbari, A. A., Le, T., Xiao, H. Y., Li, J., & Hom, E. F. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01907 · Cited by 35
AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Trepte, P., Secker, C., Olivet, J., Blavier, J., Kostova, S., Maseko, S. B., Minia, I., Silva Ramos, E., Cassonnet, P., Golusik, S., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00019-8 · Cited by 35
“While we were limited to structure prediction-based PPI mapping for complexes with up to 1400 amino acids, further improvements in the structure prediction algorithms regarding speed, memory usage and the maximal size of proteins and protein complexes are already available (Ahdritz et al, 2022).”
Deep learning methods for protein structure prediction
Qin, Y., Chen, Z., Ye, P., Xiao, Y., Zhong, T., & Yu, X. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.96 · Cited by 33
Mukherjee, S., Moafinejad, S. N., Badepally, N. G., Merdas, K., & Bujnicki, J. M. (2024) · Structure · doi:10.1016/j.str.2024.08.015 · Cited by 32
NMR investigations of glycan conformation, dynamics, and interactions
Angulo, J., Ardá, A., Bertuzzi, S., Canales, Á., Ereño‐Orbea, J., Gimeno, A., Gómez‐Redondo, M., Muñoz-García, J. C., Oquist-Phillips, P., Monaco, S., et al. (2024) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2024.10.002 · Cited by 31
Leveraging protein structural information to improve variant effect prediction
Gerasimavicius, L., Teichmann, S. A., & Marsh, J. A. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103023 · Cited by 34
Wu, T., Stein, R. A., Kao, T., Brown, B. P., & Mchaourab, H. S. (2025) · Nature Communications · doi:10.1038/s41467-025-62582-4 · Cited by 34
“To fully leverage the potential of DEER data in protein structure prediction, we have developed DEERFold, a framework that directly incorporates spin label distance distributions derived from DEER data into AlphaFold2 through OpenFold 52 .”
InfluentialEllaway, J. I. J., Anyango, S., Nair, S. S., Zaki, H. A., Nadzirin, N., Powell, H. R., Gutmanas, A., Váradi, M., & Velankar, S. S. (2024) · Structural Dynamics · doi:10.1063/4.0000251 · Cited by 28
Artificial intelligence methods for protein folding and design
Zhang, Z., Ou, C., Cho, Y., Akiyama, Y., & Ovchinnikov, S. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103066 · Cited by 29
Self-citationArtificial intelligence-driven computational methods for antibody design and optimization
Vecchietti, L. F., Wijaya, B. N., Armanuly, A., Hangeldiyev, B., Jung, H., Lee, S., Cha, M., & Kim, H. M. (2025) · mAbs · doi:10.1080/19420862.2025.2528902 · Cited by 32
“158 Moreover, methods like TAP 159 and PropertyDAG 160 have been developed to predict CDR-related developability properties and rank design candidates, respectively.”
Metamorphic proteins and how to find them
Porter, L. L., Artsimovitch, I., & Ramírez‐Sarmiento, C. A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102807 · Cited by 29
Bedraoui, A., Suntravat, M., Mejjad, S. E., Enezari, S., Oukkache, N., Sanchez, E. E., Galan, J. A., Fatimy, R. E., & Daouda, T. (2023) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2023.100175 · Cited by 28
Heinzinger, M., & Rost, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102986 · Cited by 27
Aubel, M., Eicholt, L. A., & Bornberg‐Bauer, E. (2023) · F1000Research · doi:10.12688/f1000research.130443.1 · Cited by 24
Structure-based prediction of protein-nucleic acid binding using graph neural networks
Sagendorf, J. M., Mitra, R., Huang, J., Chen, X. S., & Rohs, R. (2024) · Biophysical Reviews · doi:10.1007/s12551-024-01201-w · Cited by 26
“In addition to experimentally determined structures, advances in protein structure prediction (Baek et al. 2021; Jumper et al. 2021; Ahdritz et al. 2024) offer vast amounts of accurately predicted protein structures that can be analyzed with computational methods.”
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
O’Donnell, T. J., Kanduri, C., Isacchini, G., Limenitakis, J. P., Brachman, R. A., Alvarez, R. A., Haff, I. H., Sandve, G. K., & Greiff, V. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.006 · Cited by 24
Self-citationAdvanced Strategies in Phage Research: Innovations, Applications, and Challenges
Wu, P., Li, W., Zhang, W., Li, S., Deng, B., Xu, S., & Li, Z. (2025) · Microorganisms · doi:10.3390/microorganisms13081960 · Cited by 25
“Structure prediction platforms such as AlphaFold and OpenFold have resolved previously uncharacterized proteins, including endolysins and tail fibers, enabling residue-level engineering [16,17].”
InfluentialDeep generative modeling of temperature-dependent structural ensembles of proteins
Janson, G., Jussupow, A., & Feig, M. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01737-2 · Cited by 33
“The decoderisbasedontheStructureModulefromAF2,with fi veblocks 1,64 .”
Target identification and assessment in the era of AI
Pun, F. W., Podolskiy, D. I., Izumchenko, E., Mortlock, A. A., Oprea, T. I., Scheibye‐Knudsen, M., Fortney, K., Morgen, E. K., Ren, F., & Zhavoronkov, A. A. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01412-8 · Cited by 31
“Partly in response to AlphaFold2, the OpenFold Consortium developed OpenFold to provide open access to its training code and data, while achieving comparable prediction accuracy 158 .”
Random, de novo, and conserved proteins: How structure and disorder predictors perform differently
Middendorf, L., & Eicholt, L. A. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26652 · Cited by 24
“34,35 35 – 37 Being based on the structure module of AF2, all modern structure predictors provide a per residue confidence score established with AF2; pLDDT (predicted local distance difference test).”
BackgroundIntegrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps
Dai, X., Wu, L., Yoo, S., & Liu, Q. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad405 · Cited by 22
MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction
Glukhov, E., Kalitin, D., Stepanenko, D., Zhu, Y., Nguyen, T., Jones, G. S., Patsahan, T., Simmerling, C. L., Mitchell, J. C., Vajda, S., et al. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.05.011 · Cited by 23
Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00222 · Cited by 17
AI meets physics in computational structure-based drug discovery for GPCRs
Michino, M., Vendôme, J., & Kufareva, I. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00019-0 · Cited by 27
Mieres‐Pérez, J., Almeida‐Hernández, Y., Sander, W., & Sánchez‐García, E. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00807 · Cited by 22
Song, C., Gu, J., Ren, H., Li, Z., Xie, D., Han, L., Qiao, J., Cheng, Z., Nie, Y., & Zhou, Z. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108716 · Cited by 21
Agentic AI and the rise of in silico team science in biomedical research
Li, B., Saini, A. K., Hernandez, J. G., & Moore, J. H. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03035-1 · Cited by 29
Large Language Models in Genomics—A Perspective on Personalized Medicine
Ali, S. M., Qadri, Y. A., Ahmad, K., Lin, Z., Leung, M.-F., Kim, S. W., Vasilakos, A. V., & Zhou, T. (2025) · Bioengineering · doi:10.3390/bioengineering12050440 · Cited by 26
Deep Learning for Flexible and Site-Specific Protein Docking and DesignPreprint
McPartlon, M., & Xu, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.01.535079 · Cited by 24
ESMFold Hallucinates Native-Like Protein SequencesPreprint
Jeliazkov, J. R., del Alamo, D., & Karpiak, J. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.23.541774 · Cited by 24
“Our approach to enabling design of input sequences in the ESMFold network, which is built on OpenFold [15] and is outlined in Figure 1, is no different: design is achieved by minimizing the loss gradient with respect to the input rather than to the weights.”
MethodModelling protein complexes with crosslinking mass spectrometry and deep learning
Stahl, K., Demann, L., Bremenkamp, R., Warneke, R., Hormes, B., Stülke, J., Brock, O., & Rappsilber, J. (2023) · doi:10.1101/2023.06.07.544059 · Cited by 20
The outcast of medicine: metals in medicine--from traditional mineral medicine to metallodrugs
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Self-citationThe Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe
Zhang, G., Liu, C., Lu, J., Zhang, S., & Zhu, L. (2025) · Biology · doi:10.3390/biology14091268 · Cited by 22
“OpenFold [71] provides an open-source reimplemen-tation of AF2 with a comparable accuracy but a faster speed and lower memory usage, while SPIRED [72] can address high-throughput demands by delivering significant speed improvements and reduced computational costs while achieving a performance…”
Self-citationAssessing protein model quality based on deep graph coupled networks using protein language model
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Self-citationPredicting TCR sequences for unseen antigen epitopes using structural and sequence features
Ji, H., Wang, X.-X., Zhang, Q., Zhang, C., & Zhang, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae210 · Cited by 17
“OpenFold [19] is an open-source PyTorch reimplementation of AlphaFold2 [16] trained from scratch.”
Zhang, Z., Qian, H., Xu, R., Duan, Q., Wang, Y., Fu, T., Wu, X., Sha, C., Wu, Q., Wang, Y., et al. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c11823 · Cited by 17
Self-citation(2024) · Nature Methods · doi:10.1038/s41592-024-02391-7 · Cited by 17
Who will make AlphaFold3 open source? Scientists race to crack AI model
Callaway, E. (2024) · Nature · doi:10.1038/d41586-024-01555-x · Cited by 17
Metadata
- Authors
- Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M. M., Zhang, S., Ojewole, A., Guney, M. E., Biderman, S., Watkins, A. M., Ra, S., Lorenzo, P. R., Nivon, L., Weitzner, B., Ban, Y.-E. A., Chen, S., Zhang, M., Li, C., Song, S. L., He, Y., Sorger, P. K., Mostaque, E., Zhang, Z., Bonneau, R., & AlQuraishi, M.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Icahn School of Medicine at Mount Sinai (US)
- Courant Institute of Mathematical Sciences (US)
- Flatiron Institute (US)
- University of Utah (US)
- Nvidia (United States) (US)
- Booz Allen Hamilton (United States) (US)
- Genentech (US)
- Arzeda (United States) (US)
- Rutgers, The State University of New Jersey (US)
- University of Illinois Urbana-Champaign (US)
- Microsoft (United States) (US)
- Corresponding author
- Bouatta, Nazim
- AlQuraishi, Mohammed
- ORCID
- Ahdritz, Gustaf
- Bouatta, Nazim
- Floristean, Christina
- Kadyan, Sachin
- Xia, Qinghui
- Gerecke, William
- O’Donnell, Timothy J.
- Berenberg, Daniel
- Fisk, Ian
- Zanichelli, Niccolò
- Zhang, Bo
- Nowaczynski, Arkadiusz
- Wang, Bei
- Stepniewska-Dziubinska, Marta M.
- Zhang, Shang
- Ojewole, Adegoke
- Biderman, Stella
- Watkins, Andrew M.
- Ra, Stephen
- Lorenzo, Pablo Ribalta
- Weitzner, Brian
- Ban, Yih-En Andrew
- Chen, Shiyang
- Zhang, Minjia
- Song, Shuaiwen Leon
- He, Yuxiong
- Sorger, Peter K.
- Zhang, Zhao
- Bonneau, Richard
- AlQuraishi, Mohammed
- Venue
- Nature Methods
- Published
- 2024-05-14
- Volume
- 21
- Issue
- 8
- Pages
- 1514-1524
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11645889
- OpenAlex
- W4396895148
- Cited by
- 439
- References
- 75
- Citations by year
- 2026: 133 · 2025: 138 · 2024: 107
- FWCI
- 51.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM150546)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54-CA225088)
- National Science Foundation (OAC-2106661, OAC-2112606)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- AlphaFold2, protein structure prediction, protein folding, model generalization, secondary structure elements, learning mechanisms, hierarchical folding
- MeSH terms
- Algorithms, Models, Molecular, Protein Conformation, Proteins, Software, Protein Structure, Secondary, Protein Folding, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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