Robust deep learning based protein sequence design using ProteinMPNN

Attention Is All You Need

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HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α‑Helical Structures

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Unraveling the Interplay between Stability and Flexibility in the Design of Polyethylene Terephthalate (PET) Hydrolases

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Emerging frontiers in protein structure prediction following the AlphaFold revolution

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Machine learning to predict de novo protein–protein interactions

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Computationally designed mRNA-launched protein nanoparticle immunogens elicit protective antibody and T cell responses in mice

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Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation

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Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm

Kshirsagar, M., Meller, A., Humphreys, I. R., Sledzieski, S., Xu, Y., Dodhia, R., Horvitz, E., Berger, B., Bowman, G. R., Ferres, J. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57148-3 · Cited by 23

FLAb: Benchmarking deep learning methods for antibody fitness predictionPreprint

Chungyoun, M., Ruffolo, J. A., & Gray, J. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.13.575504 · Cited by 27

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactionsPreprint

Lu, W., Zhang, J., Rao, J., Zhang, Z., & Zheng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.25.595871 · Cited by 27

Training Compute-Optimal Protein Language ModelsPreprint

Cheng, X., Chen, B., Li, P., Gong, J., Tang, J., & Song, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.06.597716 · Cited by 28

Reengineering of a flavin‐binding fluorescent protein using ProteinMPNN

Nikolaev, A., Kuzmin, A., Markeeva, E., Kuznetsova, E., Ryzhykau, Y. L., Semenov, O., Anuchina, A. A., Remeeva, A., & Gushchin, I. (2024) · Protein Science · doi:10.1002/pro.4958 · Cited by 21

Large-scale discovery, analysis and design of protein energy landscapes

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Protease engineering: Approaches, tools, and emerging trends

Martinusen, S. G., Nelson, S. E., Slaton, E. W., Long, L. F., Pho, R., Ajayebi, S., & Denard, C. A. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108602 · Cited by 23

Bond-centric modular design of protein assemblies

Wang, S., Favor, A., Kibler, R. D., Lubner, J. M., Borst, A. J., Coudray, N., Redler, R. L., Chiang, H. T., Sheffler, W., Hsia, Y., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02297-5 · Cited by 22

De novo design of light-responsive protein–protein interactions enables reversible formation of protein assemblies

Yu, B., Liu, J., Cui, Z., Wang, C., Chen, P., Wang, C., Zhang, Y., Zhu, X., Zhang, Z., Li, S., et al. (2025) · Nature Chemistry · doi:10.1038/s41557-025-01929-2 · Cited by 29

Design of facilitated dissociation enables timing of cytokine signalling

Broerman, A. J., Pollmann, C., Zhao, Y., Lichtenstein, M. A., Jackson, M. D., Tessmer, M. H., Ryu, W. H., Ogishi, M., Abedi, M. H., Sahtoe, D. D., et al. (2025) · Nature · doi:10.1038/s41586-025-09549-z · Cited by 32

Generalizable and scalable protein stability prediction with rewired protein generative models

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Unified modeling of 3D molecular generation via atomic interactions with PocketXMol

Peng, X., Guo, R., Guo, F., Wang, Z., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M., et al. (2026) · Cell · doi:10.1016/j.cell.2026.01.003 · Cited by 22

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Wang, X., Xu, K., Tan, Y., Liu, S., & Zhou, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043827 · Cited by 22

Determinants for an Efficient Enzymatic Catalysis in Poly(Ethylene Terephthalate) Degradation

Castro-Rodríguez, J. A., Rodríguez‐Sotres, R., & Farrés, A. (2023) · Catalysts · doi:10.3390/catal13030591 · Cited by 25

Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN

Kao, H.-W., Lu, W.-L., Ho, M., Lin, Y., Hsieh, Y.-J., Ko, T., Hsu, S. D., & Wu, K. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00042 · Cited by 21

In vivo functional phenotypes from a computational epistatic model of evolution

Alvarez, S., Nartey, C. M., Mercado, N., de la Paz, J. A., Huseinbegovic, T., & Morcos, F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308895121 · Cited by 20

Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen

Hutchinson, M., Ruffolo, J. A., Haskins, N., Iannotti, M., Vozza, G., Pham, T., Mehzabeen, N., Shandilya, H., Rickert, K., Croasdale-Wood, R., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2362775 · Cited by 22

De novo design of high-affinity protein binders to bioactive helical peptides

Vázquez Torres, S., Leung, P. J. Y., Lutz, I. D., Venkatesh, P., Watson, J. L., Hink, F., Huynh, H.-H., Yeh, A. H.-W., Juergens, D., Bennett, N. R., et al. (2022) · doi:10.1101/2022.12.10.519862 · Cited by 22

Simple computational methods can outperform deep learning in designing diverse, binder-enriched antibody librariesPreprint

Chinery, L., Hummer, A. M., Mehta, B. B., Akbar, R., Rawat, P., Slabodkin, A., Quý, K. L., Lund‐Johansen, F., Greiff, V., Jeliazkov, J. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.26.586756 · Cited by 22

De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptorsPreprint

Muratspahić, E., Feldman, D., Kim, D. E., Qu, X.-M., Bratovianu, A., Rivera-Sánchez, P., Dimitri, F., Cao, J., Cary, B. P., Belousoff, M., et al. (2025) · bioRxiv · doi:10.1101/2025.03.23.644666 · Cited by 25

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Kim, H., Hillson, N. J., Cho, B., Sung, B. H., Lee, D., Kim, D., Oh, M., Chang, M. W., Jin, Y., Rosser, S. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61263-6 · Cited by 24

Protein structure and dynamics in the era of integrative structural biology

Grandori, R. (2023) · Frontiers in Biophysics · doi:10.3389/frbis.2023.1219843 · Cited by 22

DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure

Yu, G., Zhao, Q., Bi, X., & Wang, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae232 · Cited by 23

An integrative approach to protein sequence design through multiobjective optimization

Hong, L., & Kortemme, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011953 · Cited by 25

Leveraging large language models for peptide antibiotic design

Guan, C., Fernandes, F. C., Franco, O. L., & de la Fuente‐Núñez, C. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102359 · Cited by 23

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

Kim, D. E., Watson, J. L., Juergens, D., Majumder, S., Sonigra, R., Gerben, S. R., Kang, A., Bera, A. K., Li, X., & Baker, D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2425459122 · Cited by 21

From discovery to the clinic: structural insights, engineering options, clinical, and ‘next wave’ applications of camelid-derived single-domain antibodies

Evers, A., Guarnera, E., Pekar, L., & Zielonka, S. (2025) · mAbs · doi:10.1080/19420862.2025.2583210 · Cited by 23

Learned features of antibody-antigen binding affinity

Miller, N. L., Clark, T., Raman, R., & Sasisekharan, R. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1112738 · Cited by 22

Self-assembly systems to troubleshoot metabolic engineering challenges

Chen, Z., Wu, T., Yu, S., Li, M., Fan, X., & Huo, Y. (2023) · Trends in biotechnology · doi:10.1016/j.tibtech.2023.06.009 · Cited by 23

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Tu, G., Fu, T., Zheng, G., Xu, B., Gou, R., Luo, D., Wang, P., & Xue, W. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01736 · Cited by 23

A de novo designed coiled coil-based switch regulates the microtubule motor kinesin-1

Cross, J. A., Dawson, W., Shukla, S., Weijman, J. F., Mantell, J., Dodding, M. P., & Woolfson, D. N. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01640-2 · Cited by 19

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models

Tan, Y., Li, M., Zhou, B., Zhong, B., Zheng, L., Tan, P., Zhou, Z., Yu, H., Fan, G., & Hong, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00689 · Cited by 23

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

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

Zimmerman, L., Alon, N., Levin, I., Koganitsky, A., Shpigel, N., Brestel, C., & Lapidoth, G. D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313809121 · Cited by 22

Zero-shot transfer of protein sequence likelihood models to thermostability prediction

Reeves, S., & Kalyaanamoorthy, S. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00887-7 · Cited by 22

DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning

Savojardo, C., Manfredi, M., Martelli, P. L., & Casadio, R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btaf019 · Cited by 22

The resistance awakens: Diversity at the DNA, RNA, and protein levels informs engineering of plant immune receptors from Arabidopsis to crops

Sutherland, C. A., Stevens, D. M., Seong, K., Wei, W., & Krasileva, K. V. (2025) · The Plant Cell · doi:10.1093/plcell/koaf109 · Cited by 20

Phage therapy against Klebsiella pneumoniae: An evolving perspective

Xing, J., Zhang, H., Zheng, L., Zhao, J., Zhang, Y., Xu, Z., Zhai, Y., Hu, G., Li, J., & Wu, H. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108689 · Cited by 22

Protein language models trained on biophysical dynamics inform mutation effects

Hou, C., Zhao, H., & Shen, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2530466123 · Cited by 27

Protein language model-embedded geometric graphs power inter-protein contact prediction

Si, Y., & Yan, C. (2024) · eLife · doi:10.7554/elife.92184.3

Extracellular vesicles for developing targeted hearing loss therapy

Pan, X., Li, Y., Huang, P., Staecker, H., & He, M. (2024) · Journal of Controlled Release · doi:10.1016/j.jconrel.2023.12.050 · Cited by 23

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling

Ding, X., Chen, X., Sullivan, E. E., Shay, T. F., & Gradinaru, V. (2024) · Molecular Therapy · doi:10.1016/j.ymthe.2024.04.003 · Cited by 20

Structure‐based computational design of antibody mimetics: challenges and perspectives

Chaves, E. J. F., Coêlho, D. F., da Cruz, C. H. B., Moreira, E. G., Simões, J., Nascimento‐Filho, M. J., & Lins, R. D. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13855 · Cited by 22

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications

Tan, Y., Li, M., Zhou, Z., Tan, P., Yu, H., Fan, G., & Liang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00884-3 · Cited by 22

Network model with internal complexity bridges artificial intelligence and neuroscience

He, L., Xu, Y., He, W., Lin, Y., Tian, Y., Wu, Y., Wang, W., Zhang, Z., Han, J., Tian, Y., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00674-9 · Cited by 22

p-IgGen: a paired antibody generative language model

Turnbull, O. M., Oglić, D., Croasdale-Wood, R., & Deane, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae659 · Cited by 27

Computational design of soluble functional analogues of integral membrane proteinsPreprint

Goverde, C. A., Pačesa, M., Goldbach, N. M., Dornfeld, L., Balbi, P. E. M., Georgeon, S., Rosset, S., Kapoor, S., Choudhury, J., Dauparas, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.09.540044 · Cited by 20

Is Novelty Predictable?Preprint

Fannjiang, C., & Listgarten, J. (2023) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041469 · Cited by 21

Fast and accurate modeling of TCR-peptide-MHC complexes using tFold-TCRPreprint

Wu, F., Zhao, Y. S., Xiao, Y., Qin, C., Wang, F., Wu, Z., Huang, L.-K., Liu, X., Song, J., He, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.12.632367 · Cited by 20

Transformation of a Viral Capsid from Nanocages to Nanotubes and Then to Hydrogels: Redirected Self-Assembly and Effects on Immunogenicity

Yang, M., Rao, G., Li, L., Qi, L., Ma, C., Zhang, H., Gong, J. Y., Wei, B., Zhang, X., Chen, G., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c01969 · Cited by 19

ESM ‐scan—A tool to guide amino acid substitutions

Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G. (2024) · Protein Science · doi:10.1002/pro.5221 · Cited by 19

Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis

Vornholt, T., Stockinger, P., Mutný, M., Jeschek, M., Nestl, B. M., Oberdorfer, G., Osuna, S., Pleiss, J., Welner, D. H., Krause, A., et al. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00949 · Cited by 23

Artificial intelligence for radiopharmaceutical and molecular imaging

Tao, J., Liang, L., Hao, S., Chen, Y., Yang, Z., Cai, Y., & Zhu, H. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.039 · Cited by 23

De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Mallik, B. B., Stanislaw, J., Alawathurage, T. M., & Khmelinskaia, A. (2023) · ChemBioChem · doi:10.1002/cbic.202300117 · Cited by 21

A suite of designed protein cages using machine learning and protein fragment-based protocols

Meador, K., Castells-Graells, R., Aguirre, R., Sawaya, M. R., Arbing, M. A., Sherman, T., Senarathne, C., & Yeates, T. O. (2024) · Structure · doi:10.1016/j.str.2024.02.017 · Cited by 19

Virus-like particles derived from bacteriophage MS2 as antigen scaffolds and RNA protective shells

Naskalska, A., & Heddle, J. G. (2024) · Nanomedicine · doi:10.2217/nnm-2023-0362 · Cited by 19

Protein A-like Peptide Design Based on Diffusion and ESM2 Models

Zhao, L., He, Q., Song, H., Zhou, T., Luo, A., Wen, Z., Wang, T., & Lin, X. (2024) · Molecules · doi:10.3390/molecules29204965 · Cited by 20

Engineered enzymes for enantioselective nucleophilic aromatic substitutions

Lister, T. M., Roberts, G. W., Hossack, E. J., Zhao, F., Burke, A. J., Johannissen, L. O., Hardy, F. J., Millman, A. A. V., Leys, D., Larrosa, I., et al. (2025) · Nature · doi:10.1038/s41586-025-08611-0 · Cited by 21

Unraveling the molecular basis of substrate specificity and halogen activation in vanadium-dependent haloperoxidases

Zeides, P., Bellmann‐Sickert, K., Zhang, R., Seel, C. J., Most, V., Schoeder, C. T., Groll, M., & Gulder, T. (2025) · Nature Communications · doi:10.1038/s41467-025-57023-1 · Cited by 21

The macromolecular crystallography beamlines of the Helmholtz-Zentrum Berlin at the BESSY II storage ring: history, current status and future directions

Müeller, U., Barthel, T., Benz, L. S., Bon, V., Crosskey, T., Dieguez, C. G., Förster, R., Gless, C., Hauß, T., Heinemann, U., et al. (2025) · Journal of Synchrotron Radiation · doi:10.1107/s1600577525001110 · Cited by 24

DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks

Wang, Y., Cheng, L., Zhang, Y., Cao, Y., & Alghazzawi, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf187 · Cited by 22

Designed miniproteins potently inhibit and protect against MERS-CoV

Ragotte, R. J., Tortorici, M. A., Catanzaro, N., Addetia, A., Coventry, B., Froggatt, H. M., Lee, J., Stewart, C., Brown, J. T., Goreshnik, I., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115760 · Cited by 18

The 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

Discovery and protein language model-guided design of hyperactive transposases

Ivančić, D., Agudelo, A., Lindstrom-Vautrin, J., Jaraba-Wallace, J., Gallo, M., Das, R., Ragel, A., Herrero-Vicente, J., Higueras, I., Billeci, F., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02816-4 · Cited by 22

Improved protein binder design using β-pairing targeted RFdiffusion

Sappington, I., Toul, M., Lee, D. S., Robinson, S. A., Goreshnik, I., McCurdy, C., Chan, T. C., Buchholz, N., Huang, B., Vafeados, D. K., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67866-3 · Cited by 33

Guidelines for designing peptoid structures: Insights from the Peptoid Data Bank

Eastwood, J. R. B., Weisberg, E. I., Katz, D., Zuckermann, R. N., & Kirshenbaum, K. (2023) · Peptide Science · doi:10.1002/pep2.24307 · Cited by 18

Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks

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Exploiting conformational dynamics to modulate the function of designed proteins

Rennella, E., Sahtoe, D. D., Baker, D. A., & Kay, L. E. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303149120 · Cited by 18

Understanding Protein Functions in the Biological Context

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Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

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Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins

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Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches

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Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Khmelinskaia, A., Bethel, N. P., Fatehi, F., Mallik, B. B., Antanasijevic, A., Borst, A. J., Lai, S.-H., Chim, H. Y., Wang, J. Y. ‘., Miranda, M. C., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01490-z · Cited by 18

Assessing Generative Model Coverage of Protein Structures with SHAPES

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Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineeringPreprint

Gantz, M., Mathis, S. V., Nintzel, F. E. H., Penner, M., Zurek, P. J., Knaus, T., Tseliou, V., Patel, E., Boros, D., Weberling, F.-M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.08.588565 · Cited by 22

Computational Design of MetallohydrolasesPreprint

Kim, D., Woodbury, S. M., Ahern, W., Tischer, D., Hanikel, N., Salike, S., Yim, J., Pellock, S. J., Lauko, A., Kalvet, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.13.623507 · Cited by 18

Mixture of Experts Enable Efficient and Effective Protein Understanding and DesignPreprint

Sun, N., Zou, S., Tao, T., Mahbub, S., Li, D., Zhuang, Y., Wang, H., Cheng, X., Song, L., & Xing, E. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.29.625425 · Cited by 26

ctGAN: combined transformation of gene expression and survival data with generative adversarial network

Kim, J., & Seok, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae325 · Cited by 17

Rational Design Principles for De Novo α-Helical Peptide Barrels with Dynamic Conductive Channels

Niitsu, A., Thomson, A. R., Scott, A. J., Sengel, J. T., Jung, J., Mahendran, K. R., Sodeoka, M., Bayley, H., Sugita, Y., Woolfson, D. N., et al. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c13933 · Cited by 19

Enzyme functional classification using artificial intelligence

Kim, H. R., Ji, H., Kim, G. B., & Lee, S. Y. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.03.003 · Cited by 18

Recent progress and future challenges in structure-based protein-protein interaction prediction

Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 25

From machine learning to multimodal models: The AI revolution in enzyme engineering

Shi, Z., Xu, S., Xue, S., Chen, K., Lu, Y., Wang, F., Long, S., Tian, Y., Zhang, P., Wang, J., et al. (2025) · BioDesign Research · doi:10.1016/j.bidere.2025.100044 · Cited by 19

Bottom-up design of Ca2+ channels from defined selectivity filter geometry

Liu, Y., Weidle, C., Mihaljević, L., Watson, J. L., Li, Z., Yu, L. T., Majumder, S., Borst, A. J., Carr, K. D., Kibler, R. D., et al. (2025) · Nature · doi:10.1038/s41586-025-09646-z · Cited by 20

LDBT instead of DBTL: combining machine learning and rapid cell-free testing

Clark‐ElSayed, A., Harrison, I. M., Olsen, M. L., Lazar, J. T., Jewett, M. C., & Ellington, A. D. (2025) · Nature Communications · doi:10.1038/s41467-025-65281-2 · Cited by 19

Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters

Malli, A., Vasyutyn, D., & Kim, J. R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02428 · Cited by 19

Generative design of bacteriophages with genome language models

King, S. B., Driscoll, C. L., Li, D. B., Guo, D., Merchant, A. T., Brixi, G., Wilkinson, M. E., & Hie, B. (2026) · Science · doi:10.1126/science.aec2657 · Cited by 87

Rational design of small‐molecule responsive protein switches

Shui, S., Buckley, S., Scheller, L., & Correia, B. E. (2023) · Protein Science · doi:10.1002/pro.4774 · Cited by 17

Validation of de novo designed water‐soluble and transmembrane β‐barrels by in silico folding and melting

Hermosilla, A. M., Berner, C., Ovchinnikov, S., & Vorobieva, A. A. (2024) · Protein Science · doi:10.1002/pro.5033 · Cited by 20

Artificial Spider Silk Materials: From Molecular Design, Mesoscopic Assembly, to Macroscopic Performances

Shi, Y., Zhu, Y., Qian, Z., & Xia, X. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202412793 · Cited by 16

From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation

Durán, J. M., van der Vloed, D., Ruifrok, A. C. C., & Ypma, R. J. F. (2024) · Forensic Science International Synergy · doi:10.1016/j.fsisyn.2024.100554 · Cited by 24

An outlook on structural biology after A lpha F old: tools, limits and perspectives

Rosignoli, S., Pacelli, M., Manganiello, F., & Paiardini, A. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13902 · Cited by 21

Microbiome regulation for sustainable wastewater treatment

Shi, K., Xu, J., Cui, H., Cheng, H.-Y., Liang, B., & Wang, A. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108458 · Cited by 18

GRACE: Generative Redesign in Artificial Computational Enzymology

Hu, R.-E., Yu, C., & Ng, I. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00624 · Cited by 19

[Re] Robust deep learning–based protein sequence design using ProteinMPNNPreprint

Huang, C., Ahn, R., & Lu, W. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21889868

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition

(2024) · doi:10.1093/bib/bbae146 · Cited by 6

Atomic context-conditioned protein sequence design using LigandMPNN

Dauparas, J., Lee, G. R., Pecoraro, R. J., An, L., Anishchenko, I. V., Glasscock, C. J., & Baker, D. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02626-1 · Cited by 341

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrelsPreprint

Dolorfino, M., Samanta, R., & Vorobieva, A. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.16.575764 · Cited by 6

Protein sequence design with a learned potential

Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171