Efficient protein structure generation with sparse denoising models
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Abstract
Proteins play diverse roles in all domains of life and are extensively harnessed as biomolecules in biotechnology, with applications spanning from fundamental research to biomedicine. Therefore, there is considerable interest in computationally designing proteins with specified properties. Protein structure generative models provide a means to design protein structures in a controllable manner and have been successfully applied to address various protein design tasks. Such models are paired with protein sequence and structure predictors to produce and select protein sequences for experimental testing. However, current protein structure generators face important limitations for proteins with more than 400 amino acids and require retraining for protein design tasks unseen during model training. To address the first issue, we introduce salad, a family of sparse all-atom denoising models for protein structure generation. Our models are smaller and faster than the state of the art and matching or improving design quality, successfully generating structures for protein lengths up to 1,000 amino acids. To address the second issue, we combine salad with structure editing, a sampling strategy for expanding the capability of protein denoising models to unseen tasks. We apply our approach to a variety of challenging protein design tasks, from generating protein scaffolds containing functional protein motifs (motif scaffolding) to designing proteins capable of adopting multiple distinct folds under different conditions (multi-state protein design), demonstrating the flexibility of salad and structure editing.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
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
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
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
Neural Discrete Representation Learning
Oord, A. V. D., Vinyals, O., & Kavukcuoglu, K. (2017) · arXiv · doi:10.48550/arxiv.1711.00937 · Cited by 2,729 · 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
Berman, H. M., Battistuz, T., Bhat, T. N., Bluhm, W. F., Bourne, P. E., Burkhardt, K., Feng, Z., Gilliland, G. L., Iype, L. E., Jain, S. C., et al. (2002) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444902003451 · Cited by 2,578 · see citing works
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369 · see citing works
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1× · see citing works
“Genie 2 was trained on a clustered subset of the AlphaFold database, which greatly exceeds our PDB dataset in both size and structural diversity 2,54,60 .”
Leaver-Fay (2011) · doi:10.1016/b978-0-12-381270-4.00019-6 · Cited by 1,904 · see citing works
On Layer Normalization in the Transformer ArchitecturePreprint
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H.-S., Lan, Y.-Y., Wang, L.-W., & Liu, T.-Y. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2002.04745 · Cited by 1,709 · see citing works
Improved Denoising Diffusion Probabilistic Models
Nichol, A., & Dhariwal, P. (2021) · arXiv · doi:10.48550/arxiv.2102.09672 · Cited by 1,550 · see citing works
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · see citing works
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978 · see citing works
Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. (2025) · Science · doi:10.1126/science.ads0018 · Cited by 937 · see citing works
Elucidating the Design Space of Diffusion-Based Generative Models
Karras, T., Aittala, M., Aila, T., & Laine, S. (2022) · neural information processing systems · doi:10.52202/068431-1926 · Cited by 702 · 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
Proof of principle for epitope-focused vaccine design
Correia, B. E., Bates, J. T., Loomis, R. J., Baneyx, G., Carrico, C., Jardine, J. G., Rupert, P., Correnti, C., Kalyuzhniy, O., Vittal, V., et al. (2014) · Nature · doi:10.1038/nature12966 · Cited by 555 · see citing works
Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp>
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503 · see citing works
Domain combinations in archaeal, eubacterial and eukaryotic proteomes
Apic, G., Gough, J., & Teichmann, S. A. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4776 · Cited by 496 · see citing works
Scaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al. (2022) · Science · doi:10.1126/science.abn2100 · Cited by 487 · see citing works
Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06728-8 · Cited by 444 · see citing works
De novo design of luciferases using deep learning
Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I. V., et al. (2023) · Nature · doi:10.1038/s41586-023-05696-3 · Cited by 441 · see citing works
Improving de novo protein binder design with deep learning
Bennett, N. R., Coventry, B., Goreshnik, I., Huang, B., Allen, A., Vafeados, D., Peng, Y. P., Dauparas, J., Baek, M., Stewart, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38328-5 · Cited by 402 · see citing works
High-resolution <i>de novo</i> structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works
Clustering predicted structures at the scale of the known protein universe
Barrio-Hernandez, I., Yeo, J., Jänes, J., Mirdita, M., Gilchrist, C. L. M., Wein, T., Varadi, M., Velankar, S., Beltrao, P., & Steinegger, M. (2023) · Nature · doi:10.1038/s41586-023-06510-w · Cited by 362 · see citing works
De Novo Enzyme Design Using Rosetta3
Richter, F. M., Leaver‐Fay, A., Khare, S. D., Bjelic, S., & Baker, D. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019230 · Cited by 359 · see citing works
Diffusion-LM Improves Controllable Text GenerationPreprint
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., & Hashimoto, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.14217 · Cited by 318 · 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
Exploring the repeat protein universe through computational protein design
Brunette, T., Parmeggiani, F., Huang, P., Bhabha, G., Ekiert, D. C., Tsutakawa, S. E., Hura, G. L., Tainer, J. A., & Baker, D. A. (2015) · Nature · doi:10.1038/nature16162 · Cited by 252 · see citing works
Hallucinating symmetric protein assemblies
Wicky, B. I. M., Milles, L. F., Courbet, A., Ragotte, R. J., Dauparas, J., Kinfu, E., Tipps, S., Kibler, R. D., Baek, M., DiMaio, F., et al. (2022) · Science · doi:10.1126/science.add1964 · Cited by 240 · see citing works
Computational design of serine hydrolases
Lauko, A., Pellock, S. J., Sumida, K. H., Anishchenko, I., Juergens, D., Ahern, W., Jeung, J., Shida, A. F., Hunt, A., Kalvet, I., et al. (2025) · Science · doi:10.1126/science.adu2454 · Cited by 235 · see citing works
De novo protein design enables the precise induction of RSV-neutralizing antibodies
Sesterhenn, F., Yang, C., Bonet, J., Cramer, J. T., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., et al. (2020) · Science · doi:10.1126/science.aay5051 · Cited by 191 · see citing works
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Sims, J. N., Arnoldt, L., Hendel, S. J., Simma, M. K., Liu, G., Yase, M., Wu, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02395-w · Cited by 172 · see citing works
Protein generation with evolutionary diffusion: sequence is all you need
Alamdari, S., Thakkar, N., van den Berg, R., Tenenholtz, N., Strome, R., Moses, A. M., Lu, A. X., Fusi, N., Amini, A. P., & Yang, K. K. (2023) · doi:10.1101/2023.09.11.556673 · Cited by 154 · see citing works
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26617 · Cited by 148 · cited 1× · see citing works
“Evaluating these models on the CASP14 and CASP15 monomer test sets 50,51 resulted in all models reaching 1 Å reconstruction accuracy after fewer than ten recycling iterations, regardless of equivariance and distogram-based neighbours (Fig.”
Transformers without Tears: Improving the Normalization of Self-AttentionPreprint
Nguyen, T. Q., & Salazar, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05895 · Cited by 124 · see citing works
BindCraft: one-shot design of functional protein bindersPreprint
Pačesa, M., Nickel, L., Schellhaas, C., Schmidt, J. H., Pyatova, E., Kissling, L., Barendse, P., Choudhury, J., Kapoor, S., Alcaraz‐Serna, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615802 · Cited by 111 · see citing works
De novo design of allosterically switchable protein assemblies
Pillai, A. S., Idris, A., Philomin, A., Weidle, C., Skotheim, R., Leung, P. J. Y., Broerman, A. J., Demakis, C., Borst, A. J., Praetorius, F., et al. (2024) · Nature · doi:10.1038/s41586-024-07813-2 · Cited by 96 · see citing works
An all-atom protein generative model
Chu, A. E., Kim, J., Cheng, L., Nesr, G. E., Xu, M., Shuai, R. W., & Huang, P. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311500121 · Cited by 95 · see citing works
Score-based generative modeling for de novo protein design
Lee, J. S., Kim, J., & Kim, P. M. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00440-3 · Cited by 94 · see citing works
ProteinSGM: Score-based generative modeling for <i>de novo</i> protein design
Lee, J. S., Kim, J., & Kim, P. M. (2022) · doi:10.1101/2022.07.13.499967 · Cited by 94 · see citing works
Design of stimulus-responsive two-state hinge proteins
Praetorius, F., Leung, P. J. Y., Tessmer, M. H., Broerman, A., Demakis, C., Dishman, A. F., Pillai, A., Idris, A., Juergens, D., Dauparas, J., et al. (2023) · Science · doi:10.1126/science.adg7731 · Cited by 90 · see citing works
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
Anand, N., & Achim, T. (2022) · arXiv · doi:10.48550/arxiv.2205.15019 · Cited by 90 · see citing works
Illuminating protein space with a programmable generative model
Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., et al. (2022) · doi:10.1101/2022.12.01.518682 · Cited by 83 · see citing works
Scalable protein design using optimization in a relaxed sequence space
Frank, C., Khoshouei, A., Fuβ, L., Schiwietz, D., Putz, D., Weber, L., Zhao, Z., Hattori, M., Feng, S., de Stigter, Y., et al. (2024) · Science · doi:10.1126/science.adq1741 · Cited by 79 · see citing works
SE(3) diffusion model with application to protein backbone generationPreprint
Yim, J., Trippe, B. L., Bortoli, V. D., Mathieu, É., Arnaud Doucet, B., Barzilay, R., & Jaakkola, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.02277 · Cited by 68 · see citing works
Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists
Glögl, M., Krishnakumar, A., Ragotte, R. J., Goreshnik, I., Coventry, B., Bera, A. K., Kang, A., Joyce, E., Ahn, G., Huang, B., et al. (2024) · Science · doi:10.1126/science.adp1779 · Cited by 67 · see citing works
Lin, Y., Lee, M., Zhang, Z., & AlQuraishi, M. (2024) · arXiv.org · doi:10.48550/arxiv.2405.15489 · Cited by 66 · see citing works
AlphaDesign: A <i>de novo</i> protein design framework based on AlphaFold
Jendrusch, M., Korbel, J. O., & Sadiq, S. K. (2021) · doi:10.1101/2021.10.11.463937 · Cited by 65 · see citing works
Self-citationJoint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space DiffusionPreprint
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Arnoldt, L., Hendel, S. J., Sims, J. N., Li, X., & Baker, D. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.08.539766 · Cited by 48 · see citing works
Liu, Y., Wang, S., Dong, J., Chen, L.-H., Wang, X., Wang, L., Li, F., Wang, C., Zhang, J., Wang, Y., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02437-w · Cited by 47 · see citing works
In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models
Goudy, O. J., Nallathambi, A., Kinjo, T., Randolph, N. Z., & Kuhlman, B. A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2307371120 · Cited by 46 · see citing works
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 32 · see citing works
Lin, Y., & AlQuraishi, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.12485 · Cited by 30 · see citing works
Metamorphic proteins: the Janus proteins of structural biology
Madhurima, K., Nandi, B., & Sekhar, A. (2021) · Open Biology · doi:10.1098/rsob.210012 · Cited by 29 · see citing works
EvoBind: in silico directed evolution of peptide binders with AlphaFoldPreprint
Bryant, P., & Elofsson, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.23.501214 · Cited by 28 · see citing works
Hermosilla, A. M., Berner, C., Ovchinnikov, S., & Vorobieva, A. A. (2024) · Protein Science · doi:10.1002/pro.5033 · Cited by 19 · see citing works
Proteus: Exploring Protein Structure Generation for Enhanced Designability and EfficiencyPreprint
Wang, C., Qu, Y., Peng, Z., Wang, Y., Zhu, H., Chen, D., & Cao, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.10.579791 · Cited by 16 · see citing works
Computational design of serine hydrolases
Lauko, A., Pellock, S. J., Anischanka, I., Sumida, K. H., Juergens, D., Ahern, W., Shida, A., Hunt, A., Kalvet, I., Norn, C., et al. (2024) · doi:10.1101/2024.08.29.610411 · Cited by 16 · see citing works
P( all-atom ) Is Unlocking New Path For Protein DesignPreprint
Qu, W., Guan, J., Ma, R., Zhai, K., Wu, W., & Wang, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.16.608235 · Cited by 13 · see citing works
Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning
Castro, K. M., Watson, J. L., Wang, J., Southern, J., Ayardulabi, R., Georgeon, S., Rosset, S., Baker, D., & Correia, B. E. (2026) · Nature Chemical Biology · doi:10.1038/s41589-025-02083-z · Cited by 13 · see citing works
De novo protein design enables precise induction of functional antibodies in vivoPreprint
Sesterhenn, F., Yang, C., Bonet, J., Cramer, J., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/685867 · Cited by 7 · see citing works
Laboratory, E. M. B., Pečar, J., Lueck, R., & Wahlers, M. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.12785829 · Cited by 6 · see citing works
De novo design of alpha-beta repeat proteinsPreprint
Zorine, D., & Baker, D. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.15.590358 · Cited by 4 · see citing works
Raw data for "Efficient protein structure generation with sparse denoising models"
Jendrusch, M., & Korbel, J. O. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.14711579 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Yang, W., Wang, Y., & Wang, Y. (2026) · Biophysics Reviews · doi:10.1063/5.0273394 · Cited by 9 · see citing works
Generative AI for synthetic biology: Designing biological parts, circuits, and genomes
Kim, N., Carluccio, G. D., Zhang, K., & Collins, J. J. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101533 · Cited by 5 · see citing works
Protein design, generative AI and biological security
Brackmann, M., Reiners, S., Hoogendoorn, M., & Moser, M. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1817535 · Cited by 5 · see citing works
Ohnuki, J., & Okazaki, K.-I. (2026) · JACS Au · doi:10.1021/jacsau.6c00596 · Cited by 4 · see citing works
Atomic functional site scaffolding in biopolymers with RFdiffusion3
Butcher, J., Krishna, R., Mitra, R., Woodbury, S. M., Kim, D., Brent, R. I., Li, Y., Corley, N., Favor, A., Chen, A., et al. (2026) · Science · doi:10.1126/science.aeh2982 · Cited by 3 · see citing works
Agentic campaign control for high-throughput de novo binder designPreprint
Jeon, M., Song, J., Kim, J., & Zhong, E. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.22.753604 · Cited by 1 · see citing works
Machine learning approaches in protein-protein and protein-drug binding
Schlichthaerle, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-99507-8.00061-5
ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteinsPreprint
Xu, J. Q., Ren, M., Qi, N., Zhang, X., He, Z., Yu, C., & Bu, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.21.733655 · Cited by 1 · see citing works
Li, W., Cadet, X. F., Medina-Ortiz, D., Davari, M. D., Sowdhamini, R., Bessafi, M., Damour, C., Li, Y., Miranville, A., de Brevern, A. G., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167151
Machine learning and artificial intelligence in protein design
Madala, B. P., & Kumar, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44536-1.00013-4
- Authors
- Jendrusch, M., & Korbel, J. O.
- Affiliations
- Heidelberg University (DE)
- Corresponding author
- Jendrusch, Michael
- Korbel, Jan O.
- Venue
- Nature Machine Intelligence
- Published
- 2025-09-24
- Volume
- 7
- Issue
- 9
- Pages
- 1429-1445
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC7618410
- OpenAlex
- W4414459878
- Cited by
- 10
- References
- 71
- Citations by year
- 2026: 10
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
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- Nature Machine Intelligence (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
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- PubMed Central (publishedVersion, cc-by)
- Funders
- European Molecular Biology Laboratory (EMBL001)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein structure generation, protein design, salads, protein scaffolds, generative models
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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