Novel machine learning approaches revolutionize protein knowledge

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

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Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

SWISS-MODEL: homology modelling of protein structures and complexes

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Proceedings of the 24th international conference on Machine learning

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ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works

seaborn: statistical data visualization

Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310 · see citing works

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works

SCOP: A structural classification of proteins database for the investigation of sequences and structures

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UniProt: the universal protein knowledgebase in 2021

The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372 · see citing works

Cramming More Components onto Integrated Circuits (1965)

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Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

Sensitive protein alignments at tree-of-life scale using DIAMOND

Buchfink, B., Reuter, K., & Drost, H.-G. (2021) · Nature Methods · doi:10.1038/s41592-021-01101-x · Cited by 5,097 · see citing works

Protein complex prediction with AlphaFold-Multimer

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TM-align: a protein structure alignment algorithm based on the TM-score

Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · cited 3× · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 2× · see citing works

SignalP 6.0 predicts all five types of signal peptides using protein language models

Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01156-3 · Cited by 2,999 · see citing works

Prediction of Protein Secondary Structure at Better than 70% Accuracy

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Pore-forming activity and structural autoinhibition of the gasdermin family

Ding, J., Wang, K., Liu, W., She, Y., Sun, Q., Shi, J., Sun, H., Wang, D.-C., & Shao, F. (2016) · Nature · doi:10.1038/nature18590 · Cited by 2,771 · see citing works

Fast and accurate protein structure search with Foldseek

van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works

Fast and accurate protein structure search with Foldseek

van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

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HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288 · see citing works

Protein structure alignment by incremental combinatorial extension (CE) of the optimal path.

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Why are ?natively unfolded? proteins unstructured under physiologic conditions?

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SignalP 6.0 predicts all five types of signal peptides using protein language models

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Hidden Markov model speed heuristic and iterative HMM search procedure

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Protein Data Bank: the single global archive for 3D macromolecular structure data

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Protein 3D Structure Computed from Evolutionary Sequence Variation

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Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · cited 1× · see citing works

One thousand families for the molecular biologist

Chothia, C. (1992) · Nature · doi:10.1038/357543a0 · Cited by 1,012 · see citing works

ProteinBERT: a universal deep-learning model of protein sequence and function

Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac020 · Cited by 1,009 · see citing works

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932 · see citing works

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.15556 · Cited by 929 · see citing works

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesPreprint

Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2026.1 · Cited by 928 · see citing works

Clustering huge protein sequence sets in linear time

Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875 · see citing works

Efficiently Modeling Long Sequences with Structured State Spaces

Gu, A., Goel, K., & Ré, C. (2021) · arXiv · doi:10.48550/arxiv.2111.00396 · Cited by 805 · see citing works

ProtGPT2 is a deep unsupervised language model for protein design

Ferruz, N., Schmidt, S., & Höcker, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32007-7 · Cited by 795 · see citing works

Protein superfamilles and domain superfolds

Orengo, C. A., Jones, D. T., & Thornton, J. M. (1994) · Nature · doi:10.1038/372631a0 · Cited by 786 · see citing works

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

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DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

Thumuluri, V., Armenteros, J. J. A., Johansen, A. R., Nielsen, H., & Winther, O. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac278 · Cited by 723 · see citing works

A structural biology community assessment of AlphaFold2 applications

Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · cited 3× · see citing works

Protein structure alignment

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AlphaFold and Implications for Intrinsically Disordered Proteins

Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works

De novo protein design by deep network hallucination

Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629 · see citing works

MMseqs2 desktop and local web server app for fast, interactive sequence searches

Mirdita, M., Steinegger, M., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1057 · Cited by 618 · see citing works

Using Dali for Protein Structure Comparison

Holm, L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0270-6_3 · Cited by 613 · see citing works

Modeling aspects of the language of life through transfer-learning protein sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606 · see citing works

Modeling the language of life – Deep Learning Protein Sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works

Better prediction of functional effects for sequence variants

Hecht, M., Bromberg, Y., & Rost, B. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s8-s1 · Cited by 598 · see citing works

MGnify: the microbiome analysis resource in 2020

Mitchell, A. L., Almeida, A., Beracochea, M., Boland, M., Burgin, J., Cochrane, G., Crusoe, M. R., Kale, V., Potter, S. C., Richardson, L. J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1035 · Cited by 562 · see citing works

Computed structures of core eukaryotic protein complexes

Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542 · see citing works

Sampling alternative conformational states of transporters and receptors with AlphaFold2

del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · cited 1× · see citing works

CATH: increased structural coverage of functional space

Sillitoe, I., Bordin, N., Dawson, N., Waman, V. P., Ashford, P., Scholes, H. M., Pang, C. S. M., Woodridge, L., Rauer, C., Sen, N., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1079 · Cited by 509 · see citing works

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25823 · Cited by 506 · see citing works

Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold

Steinegger, M., Mirdita, M., & Söding, J. (2019) · Nature Methods · doi:10.1038/s41592-019-0437-4 · Cited by 445 · cited 1× · see citing works

Efficient evolution of human antibodies from general protein language models

Hie, B., Shanker, V. R., Xu, D., Bruun, T. U. J., Weidenbacher, P. A. B., Tang, S., Wu, W. C. H., Pak, J. E., & Kim, P. S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01763-2 · Cited by 444 · see citing works

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439 · cited 1× · see citing works

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

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Learning inverse folding from millions of predicted structuresPreprint

Hsu, C., Verkuil, R., Liu, J. J., Lin, Z., Hie, B., Sercu, T., Lerer, A. K., & Rives, A. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.10.487779 · Cited by 434 · see citing works

ECOD: an evolutionary classification of protein domains

Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · cited 2× · see citing works

Cryo-EM structure of the gasdermin A3 membrane pore

Ruan, J., Xia, S., Liu, X., Lieberman, J., & Wu, H. (2018) · Nature · doi:10.1038/s41586-018-0058-6 · Cited by 369 · see citing works

The language of proteins: NLP, machine learning & protein sequences

Ofer, D., Brandes, N., & Linial, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.022 · Cited by 345 · see citing works

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Vig, J., Madani, A., Varshney, L. R., Xiong, C., Socher, R., & Rajani, N. F. (2020) · doi:10.1101/2020.06.26.174417 · Cited by 338 · see citing works

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning

Høie, M. H., Kiehl, E. N., Petersen, B., Nielsen, M., Winther, O., Nielsen, H., Hallgren, J., & Marcatili, P. (2022) · Nucleic Acids Res. · doi:10.1093/nar/gkac439 · Cited by 325 · see citing works

PROTEIN FAMILIES AND THEIR EVOLUTION—A STRUCTURAL PERSPECTIVE

Orengo, C. A., & Thornton, J. M. (2005) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.74.082803.133029 · Cited by 322 · see citing works

What’s in a name? Why these proteins are intrinsically disordered

Dunker, A. K., Babu, M. M., Barbar, E. J., Blackledge, M. J., Bondos, S. E., Dosztányi, Z., Dyson, H. J., Forman-Kay, J. D., Fuxreiter, M., Gsponer, J. A., et al. (2013) · Intrinsically Disordered Proteins · doi:10.4161/idp.24157 · Cited by 308 · see citing works

Mega: Moving Average Equipped Gated AttentionPreprint

Ma, X., Zhou, C., Kong, X., He, J., Gui, L.-K., Neubig, G., May, J., & Zettlemoyer, L. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2209.10655 · Cited by 261 · see citing works

Generative models for graph-based protein design

Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 258 · see citing works

Thousands of proteins likely to have long disordered regions.

Romero, P. R., Zoran Obradovic, A., Kissinger, C. R., Villafranca, J. E., Garner, E. C., Guilliot, S., & Dunker, A. K. (1998) · PubMed · Cited by 242 · see citing works

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

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Learning protein sequence embeddings using information from structure

Bepler, T., & Berger, B. (2019) · arXiv · doi:10.48550/arxiv.1902.08661 · Cited by 219 · see citing works

Impact of protein conformational diversity on AlphaFold predictions

Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., et al. (2021) · doi:10.1101/2021.10.27.466189 · Cited by 196 · cited 1× · see citing works

Impact of protein conformational diversity on AlphaFold predictions

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AI revolutions in biology: The joys and perils of AlphaFold

Perrakis, A., & Sixma, T. K. (2021) · The EMBO Reports · doi:10.15252/embr.202154046 · Cited by 190 · see citing works

Origins of coevolution between residues distant in protein 3D structures

Anishchenko, I., Ovchinnikov, S., Kamisetty, H., & Baker, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702664114 · Cited by 185 · see citing works

Embeddings from protein language models predict conservation and variant effects

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AlphaFold2: A Role for Disordered Protein/Region Prediction?

Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173 · see citing works

SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning

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Embeddings from deep learning transfer GO annotations beyond homology

Littmann, M., Heinzinger, M., Dallago, C., Olenyi, T., & Rost, B. (2021) · Scientific Reports · doi:10.1038/s41598-020-80786-0 · Cited by 169 · see citing works

Protein disorder—a breakthrough invention of evolution?

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Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

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Protein language model embeddings for fast, accurate, alignment-free protein structure prediction

Weissenow, K., Heinzinger, M., & Rost, B. (2021) · doi:10.1101/2021.07.31.454572 · Cited by 139 · see citing works

DeepFam: deep learning based alignment-free method for protein family modeling and prediction

Seo, S., Oh, M., Park, Y., & Kim, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty275 · Cited by 134 · see citing works

The structural coverage of the human proteome before and after AlphaFold

Porta-Pardo, E., Ruiz-Serra, V., Valentini, S., & Valencia, A. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009818 · Cited by 131 · see citing works

Light attention predicts protein location from the language of life

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PSI-2: Structural Genomics to Cover Protein Domain Family Space

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ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence

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Protein embeddings and deep learning predict binding residues for various ligand classes

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Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

Villegas-Morcillo, A., Makrodimitris, S., van Ham, R. C. H. J., Gomez, A. M., Sanchez, V., & Reinders, M. J. T. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa701 · Cited by 118 · see citing works

Protein structure database search and evolutionary classification

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Contrastive learning on protein embeddings enlightens midnight zone

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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

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Protein Representation Learning by Geometric Structure PretrainingPreprint

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Transmembrane proteins in grape immunity: current knowledge and methodological advances

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Vince, M. J. K., Hughes, K. A., Buzuk, A., Perlstein, D. L., Viarengo‐Baker, L. A., & Whitty, A. (2025) · Current Protocols · doi:10.1002/cpz1.70085

Predicting Superconductor Critical Temperatures with Graph Convolutional Network

Kang, J., Yang, L., Zeng, J., Sun, Z. P., Lin, H., & Li, J. (2024) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn63567.2024.00152

A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM)

Varghese, D. M., Athulya, T., Mohani, V. K., & Ahmad, S. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4623-6_13

Additional file 1 of Rendering protein mutation movies with MutAmore

Weißenow, K., & Rost, B. (2023) · Figshare · doi:10.6084/m9.figshare.24797848

GATSBI: Improving context-aware protein embeddings through biologically motivated data splitsPreprint

Nayar, G., & Altman, R. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.13.705830

Protein Large Language Models Can Predict Flavivirus Protease Target Specificity

Montilla, R., Jedlicka, L. D. L., Barcick, U., Salardani, M. D. S., Chaves, A. F. A., Gallo, G., Guimaraes, M., Coelho, C. D. M., Moreira, L. S., Zelanis, A., et al. (2026) · ACS Omega · doi:10.1021/acsomega.5c13455

Analyse biogéométrique de l'évolution des protéines et des traits d'histoire de vie

Dagher, L. B. (2024) · doi:10.70675/3a39f4f2zb57dz4fb3z8990zbd5d3fe1a6bf

Optimization of the Quantum-Si Platinum Single-Molecule Protein Sequencing Platform Toward Improved Complex-Matrix Protein Identification

Łęski, T. A., Brown, S. M., Johnson, Z. T., Dean, S. N., Goldman, E. R., & Stenger, D. A. (2026) · ACS Omega · doi:10.1021/acsomega.6c01140

GATSBI: improving context-aware protein embeddings through biologically motivated data splits

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Younas, M. U., Qasim, M., Ahmad, I., Korai, S. K., Bibi, G., Ergashev, M., Alotaibi, M. S., Zulfiqar, U., & Gururani, M. A. (2026) · Current Plant Biology · doi:10.1016/j.cpb.2026.100659

StackEnsemXAI: XAI-based Stacked Ensemble Model for MTB Protein Function Prediction

Vijay, A., Tiwari, N., & Sharma, A. (2026) · Algorithms for intelligent systems · doi:10.1007/978-3-032-26919-5_27

SeqSol-FECS: An Integrated Deep Learning and Structural Modeling Framework for Protein Solubility Engineering

Guo, Q., Zhou, X.-T., Zhou, R., Fang, Z.-R., Shen, Q., Xue, Y., & Zheng, Y. (2026) · Biomacromolecules · doi:10.1021/acs.biomac.6c01393

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Davtyan, A., Khachatryan, A., & Petrosyan, R. A. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag296

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Chaaban, S., Ratkevičiūtė, G., & Lau, C. K. (2024) · The Biochemist · doi:10.1042/bio_2024_118 · Cited by 6 · see citing works

Apprehensions and emerging solutions in ML-based protein structure prediction

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BPS2025 - DeepFold 2: Accelerating protein structure prediction with optimized homolog detection and protein language models

Kim, M., Bae, H., Jo, G., Kim, K. W., Yoo, J., & Joo, K. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2024.11.1209

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Alanazi, W. T., Meng, D., & Pollastri, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.04.005 · Cited by 9 · see citing works

Machine Learning for Protein Structure Prediction and Design

Bell, D. R., & Chen, S. H. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_46