ProGen2: Exploring the boundaries of protein language models

Attention Is All You Need

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Adam: A Method for Stochastic Optimization

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MizAR 60 for Mizar 50

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Scaling Laws for Neural Language Models

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UniProt: a worldwide hub of protein knowledge

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Evolutionary-scale prediction of atomic level protein structure with a language model

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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

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Protein Language Models Expose Viral Immune Mimicry

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Evaluation of LLM-generated peptide as foundation template for discovery of effective encrypted AMPs against clinical superbugs

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Design of L-methionine γ-lyase using protein language models and molecular simulations to enhance catalytic efficiency and antitumor activity

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AbTune: layer-wise selective fine-tuning of protein language models for antibodies

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Controllable polysaccharide depolymerization by heparinases for efficient low molecular weight heparin production

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Learnable protein representations in computational biology for predicting drug-target affinity

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Deep Learning Meets Synthetic Biology: Applications Across Molecular, Cellular, and Metabolic Scales

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Decoding viral evolution through integrative bioinformatics: From genomes to global health

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Towards Safer Antimicrobial Peptide Therapeutics: A Predictive–Generative Framework Targeting ESKAPE Pathogens

Rashid, Z., Ahmed, H., Saleem, K., & Siddiqui, M. R. U. (2026) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-026-11022-w · Cited by 2

PepGen-FB: Multi-functional antimicrobial peptide generation via curriculum learning-guided multi-objective feedback optimization

Xiao, L., Li, J., Chen, Y., Qiu, C., Wang, Q., Liao, X., & Chen, J. (2026) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2026.100119 · Cited by 2

Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments

Durumeric, A. E. P., McCarty, S., Smith, J., Köhler, J., Elez, K., Raich, L., Suriana, P., & Sztain, T. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00332 · Cited by 2

Computational identification of antigen-specific T cell groups through generative epitope modeling

Ma, M., Tu, W., Vasquez-Rios, C., & Ding, J. (2026) · iScience · doi:10.1016/j.isci.2026.116505 · Cited by 2

Generative artificial intelligence for enzyme design and biocatalysis

Middendorf, L., & Ferruz, N. (2026) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2026.102728 · Cited by 3

A world model of the virtual cell

Xing, E. P., & Song, L. (2026) · Cell · doi:10.1016/j.cell.2026.08.042 · Cited by 2

Range-limited Heaps' law for functional DNA words in the human genome.

Li, W.-T., Almirantis, Y., & Provata, A. (2024) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2024.111878 · Cited by 4

An expandable synthetic library of human paired antibody sequences

Marinov, T. M., Wasdin, P. T., Jordaan, G., Janke, A. K., Abu-Shmais, A. A., & Georgiev, I. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012932 · Cited by 2

AI expands the repertoire of CRISPR-associated proteins for genome editing

Notin, P. (2025) · Nature · doi:10.1038/d41586-025-02135-3 · Cited by 2

Using protein blocks to build custom fragment libraries from protein structures

Dhingra, S., Télétchéa, S., Sowdhamini, R., Sanejouand, Y., de Brevern, A. G., Cadet, F., & Offmann, B. (2025) · Biochimie · doi:10.1016/j.biochi.2025.08.011 · Cited by 2

Evaluating Large Language Models for Predicting Protein Behavior under Radiation Exposure and Disease Conditions

Engel, R., & Park, G. (2024) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2024.bionlp-1.34 · Cited by 3

EMOCPD: Efficient Attention-Based Models for Computational Protein Design Using Amino Acid Microenvironment

Ling, X., Cai, C., Kong, D., Wei, Z., Wu, J., Wang, L., & Deng, Z. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00378 · Cited by 2

Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion

Hu, Y., Tan, Y., Han, A., Zheng, L., Hong, L., & Zhong, B. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822117 · Cited by 4

TMvisDB: resource for transmembrane protein annotation and 3D visualizationPreprint

Marquet, C., Grekova, A., Houri, L., Bernhofer, M., Soto, L. F. J., Karl, T., Heinzinger, M., Dallago, C., & Rost, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.30.518551 · Cited by 2

Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian OptimizationPreprint

Wang, Y., Wang, B., Shi, T., Fu, J., Zhou, Y., & Zhang, Z. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.06.565922 · Cited by 2

Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and ThermostabilityPreprint

Tan, Y., Zhong, B., Zheng, L., Fan, G., & Hong, L. (2024) · eLife · doi:10.7554/elife.98033.1 · Cited by 2

Towards Robust Evaluation of Protein Generative Models: A Systematic Analysis of MetricsPreprint

Страшнов, П. В., Shevtsov, A., Meshchaninov, V., Ivanova, M., Nikolaev, F., Kardymon, O. L., & Vetrov, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.25.620213 · Cited by 3

Protein2Text: Providing Rich Descriptions for Protein SequencesPreprint

Dotan, E., Lyubman, I., Bacharach, E., Pupko, T., & Belinkov, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.04.626777 · Cited by 2

Extending Prot2Token: Aligning Protein Language Models for Unified and Diverse Protein Prediction TasksPreprint

Pourmirzaei, M., Han, Y., Esmaili, F., Pourmirzaei, M., Alqarghuli, S., Chen, K., & Xu, D. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.03.641065 · Cited by 2

Mechanistic evidence that motif-gated domain recognition drives contact prediction in protein language modelsPreprint

Nainani, J., Reimer, B. M., Watts, C., Jensen, D., & Green, A. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.22.671739 · Cited by 2

Predicting dynamic expression patterns in budding yeast with a fungal DNA language modelPreprint

Chao, K.-H., Magzoub, M. M., Stoops, E. H., Hackett, S. R., Linder, J., & Kelley, D. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.19.677475 · Cited by 2

RemoteFoldSet: Benchmarking Structural Awareness of Protein Language ModelsPreprint

Ma, Z., & Bethel, N. P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.23.678152 · Cited by 2

GROQ-seq Enables Cross-site Reproducibility for High-Throughput Measurement of Protein FunctionPreprint

Spinner, A., Ross, D., Cortade, D. L., Ikonomova, S. P., Baranowski, C., Dhroso, A., Apel, A. R., Sheldon, K. N., Duquette, C., Densmore, D. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.07.716961 · Cited by 2

Calibration of in-frame indel variant effect predictors for clinical variant classificationPreprint

Abderrazzaq, H., Singh, M., Babb, L., Bergquist, T., Brenner, S. E., Pejaver, V. R., O’Donnell‐Luria, A., Predrag Radivojac, A., ClinGen Computational Working Group, & ClinGen Variant Classification Working Group (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.15.718599 · Cited by 2

Becoming fluent in proteins

Leem, J., & Galson, J. D. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.008 · Cited by 1

Protein Family Sequence Generation through ProGen2 Fine-Tuning

Hrbáň, H., & Hoksza, D. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821712 · Cited by 1

A Symbolic Regression Screening Approach Within Peptide Optimisation

Murphy, A., Kocherovsky, M., Dayan, N. A., Miralavy, I., Gilad, A. A., & Banzhaf, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90065-5_30 · Cited by 1

PepPFDPO: Multi-Objective Antimicrobial Peptide Generation using Pareto-Frontier Enhanced Direct Preference Optimization

Wang, L., Qiu, C., Xiao, L., & Chen, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356223 · Cited by 1

An ensemble pipeline, PhageHost, for phage tail fiber discovery and accurate Klebsiella pneumoniae host prediction using protein language models.

Wu, J., Wang, C., Zeng, Y., Song, Y.-Y., Xu, S., Nie, J., Wang, T.-X., Ma, J.-C., Feng, J., & Wu, L.-H. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117275 · Cited by 3

Dual-objective protein language model for discovery of non-hemolytic antimicrobial peptides from the bee-melectin sequence space

Das, A., Sen, S., Ahmadi, Z., & Debnath, M. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.178296 · Cited by 1

TaiChiNet: PCA-based Ying-Yang dilution of inter- and intra-BERT layers to represent anti-coronavirus peptides

Li, K., Ding, S., Guo, Z., Fan, Y., Liu, H., Sun, Y., Zhang, G., Zhang, R., Huang, L., & Zhou, F. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127786 · Cited by 1

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

Yu, Y., Jiang, F., Ma, X., Zhang, L., Zhong, B., Ouyang, W., Fan, G., Yu, H., Hong, L., & Li, M. (2025) · neural information processing systems · doi:10.52202/085713-4581 · Cited by 1

A Survey of Pretrained Protein Language Models

Pokharel, S., Pratyush, P., Chaudhari, M., Heinzinger, M., Caragea, D., Saigo, H., & KC, D. B. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4623-6_1 · Cited by 1

Graph with Residue-Based Cross-Modal Framework Enhances Cell Function-Related Protein Properties Prediction

Gou, W., Tan, Y., Liu, C., Li, M., Fan, G., & Yu, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00856 · Cited by 1

mCNN-GenEfflux: enhanced predicting Efflux protein and their super families by using generative proteins combined with multiple windows convolution neural networks

Hussain, M., Ou, Y., & Ho, Q. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108595 · Cited by 1

The integration of generative artificial intelligence and machine learning in healthcare: Current applications and future directions

Umar, M., Shamim, L., Musharaf, I., Tabasum, P., Malhotra, V., Farhan, K., Hidayat, A., Waqas, M., Anwar, A., Qadri, M., et al. (2025) · Journal of intelligent medicine. · doi:10.1002/jim4.70018 · Cited by 1

Design prokaryotic cis-regulatory elements using language model

Xia, Y., Sun, J., Du, X., Liang, Z., Wu, X., Shi, W., Shao, B., Guo, S., & Huo, Y. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag122 · Cited by 2

Predicting antibody–antigen affinity with a dual-level representation model

Wang, Z., Zhang, Y., Zhang, Y., Huang, J., Lu, X., Min, X., Ge, S., Zhang, J., & Xia, N. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag109 · Cited by 1

Hybrid deep learning with protein language models and dual-path architecture for predicting IDP functions

Liang, J., Luo, Y., Su, B., & Peng, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag126 · Cited by 1

An SE(3)-equivariant and dynamic multi-modal engine advancing from PTM site prediction to network understanding

Lin, Y., Wu, J., Yang, S., & Wang, Y. (2026) · Communications Chemistry · doi:10.1038/s42004-026-02062-y · Cited by 1

High-fidelity genome and prime editing enabled by the AI-designed openCRISPR-1

Hwang, H.-Y., Yi, H., Gwon, Y., Jeon, E. J., & Kim, D. (2026) · Genome Medicine · doi:10.1186/s13073-026-01682-2 · Cited by 2

The role of protein embeddings for protein–protein interaction prediction with graph neural networks

Balbi, L., Sousa, R. T., Cotovio, P. G., & Pesquita, C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag313 · Cited by 1

Machine Learning for Microbial Cell Factories: Pathway Design, Enzyme Engineering, and Metabolic Regulation

Huang, Y., Ge, R., Wu, L., Sun, Y., Wang, Z., & Cao, M. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00199 · Cited by 1

Precision gene editing: From proof-of-concept to curative therapies

Cui, T., Li, B., Cai, B., Wang, H., & Li, W. (2026) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2026.06.004 · Cited by 1

AI-Driven Protein Research: From Prediction to Design

Min, G., & Peng, H. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5249-7_13 · Cited by 1

Evolutionary profiles for protein fitness prediction

Jiao, X., Lin, S. Q., Fan, J., Liang, Z., Mao, W., Chen, H., & Shen, C. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag525 · Cited by 1

AI-Powered Synthetic Biology: Current Situation, Challenges, and Future Perspectives

Şahin, İ. O., Gökçe, N., Yildirim, D. T., Yildirim, A. B., Akalin, H., Martin, D. K., Beccari, T., Vicente, O., Radecka, I., Tchuenbou-Magaia, F. L., et al. (2026) · The EuroBiotech Journal · doi:10.2478/ebtj-2026-0015 · Cited by 1

Property guidance for protein sequence generative models with ProteinGuide

Xiong, J., Gaur, I., Lukarska, M., Nisonoff, H. M., Oltrogge, L. M., Savage, D. F., & Listgarten, J. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03207-z · Cited by 3

Data-Efficient Distal Engineering of Fluorinase Using Zero-Shot Models

Harding-Larsen, D., Lax, B. M., Weingarten, C. K., Sako, A., Mazurenko, S., & Welner, D. H. (2026) · Chem & Bio Engineering · doi:10.1021/cbe.6c00028 · Cited by 1

Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models

Ektefaie, Y., Shen, A., Jain, L., Farhat, M., & Žitnik, M. (2025) · neural information processing systems · doi:10.52202/085713-4356 · Cited by 1

How to build an AI-driven digital organism

Song, L., Segal, E., & Xing, E. P. (2026) · Nature Medicine · doi:10.1038/s41591-026-04595-0 · Cited by 1

How Much Do DNA and Protein Deep Embeddings Preserve Biological Information?

Tolloso, M., Galfrè, S. G., Pavone, A., Podda, M., Sîrbu, A., & Priami, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71671-3_15 · Cited by 1

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization

Qiu, J., Yuan, H. Q., Zhang, J., Chen, W., Wang, H., & Wang, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i13.29386 · Cited by 3

Diffeomorphic transformations for time series analysis: An efficient approach to nonlinear warping.

Lopez, I. (. M. (2003) · doi:10.15581/10171/67243 · Cited by 1

DNDesign: Enhancing Physical Understanding of Protein Inverse Folding Model via DenoisingPreprint

Lee, Y., & Kim, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.05.570298 · Cited by 1

Fine-tuning of conditional Transformers for the generation of functionally characterized enzymesPreprint

Nicolini, M., Saitto, E., Franco, R. E. J., Cavalleri, E., Mesiti, M., Galeano, A., Malchiodi, D., Paccanaro, A., Robinson, P. N., Casiraghi, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.10.607430 · Cited by 2

Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and ThermostabilityPreprint

Tan, Y., Zhong, B., Zheng, L., Fan, G., & Hong, L. (2024) · eLife · doi:10.7554/elife.98033.2 · Cited by 1

Language model generates cis- regulatory elements across prokaryotesPreprint

Xia, Y., Sun, J., Du, X., Liang, Z., Shi, W., Guo, S., & Huo, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.07.622410 · Cited by 1

Predicting microbial transcriptome using annotated genome sequencePreprint

Fu, G., Yan, Y., Zhao, Z., Chen, Y., & Shao, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.30.630741 · Cited by 2

PepSeek: Universal Functional Peptide Discovery with Cooperation Between Specialized Deep Learning Models and Large Language ModelPreprint

Gong, H., Wang, Y., Kong, Q., Li, X., Li, L., Wan, B., Zhao, Y., Zhang, J., Chen, G., Chen, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.29.641945 · Cited by 1

Separating selection from mutation in antibody language modelsPreprint

Matsen, F. A., IV, Dumm, W., Sung, K., Johnson, M. M., Rich, D., Starr, T. N., Song, Y. S., Fukuyama, J., & Haddox, H. K. (2026) · eLife · doi:10.7554/elife.109644.1 · Cited by 1

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomesPreprint

Tasmin, M., Mohanty, S., Kulkarni, S. G., Farhat, M. R., & Green, A. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702134 · Cited by 1

Ophiuchus-Ab: A Versatile Generative Foundation Model for Advanced Antibody-Based ImmunotherapyPreprint

Zhu, Y., Ma, J., Yin, M., Wu, J., Tang, L., Zhang, Z., Li, Q., Feng, S., Liu, H., Qin, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703197 · Cited by 1

EvoPool: Evolution-Guided Pooling of Protein Language Model EmbeddingsPreprint

Naderializadeh, N., & Singh, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703349 · Cited by 2

High Diversity Gene Libraries Facilitate Machine Learning Guided Exploration of Fluorescent Protein Sequence SpacePreprint

Benabbas, A., Kearns, P., Billo, A., Chisholm, L. O., & Plesa, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.01.706892 · Cited by 1

Co-designing sequence and structure of functional de novo enzymes with EnzyGen2Preprint

Song, Z., Liu, H., Zhao, Y., Yang, Y., & Li, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.02.709205 · Cited by 1

Explainable protein-protein binding affinity prediction via fine-tuning protein language modelsPreprint

Singh, H., SINGH, R. K., Srivastava, S. P., Pradhan, S., & Gorantla, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.30.715237 · Cited by 1

Discriminative Site-Directed Protein Engineering via Lightweight CASPE PlatformPreprint

Deng, Q., Qiao, J., Wang, C., Ni, X., Chang, Y., Zhao, N., Zhai, R., Cui, H., Li, X., & Jin, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.24.720551 · Cited by 1

Structure tokens sharpen the feature vocabulary of protein language modelsPreprint

Steenwyk, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.12.724593 · Cited by 1

SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and OptimizationPreprint

Tu, T., Huang, W., Li, Z., Ding, K., Yang, Y., & Luo, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.726972 · Cited by 1

ProtGPT3: an Open-source family of Promptable and Aligned Protein Language ModelsPreprint

Garibbo, M., Boxó, G., Stocco, F., Illanes-Vicioso, R., Middendorf, L., & Ferruz, N. (2026) · bioRxiv · doi:10.64898/2026.06.04.730041 · Cited by 2

Generating antimicrobial peptides via genomic transfer learningPreprint

Polloni, L., Gonteri, I., Bieniasz, K. D., & Frost, J. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732639 · Cited by 1

Complete Mutagenesis of the Genome and Proteome of ΦX174Preprint

Wei, H., Li, X., & Lehner, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.25.740675 · Cited by 1

Evaluating Tuning Strategies for Sequence Generation with Protein Language ModelsPreprint

Nathansen, A., Klein, K., Renard, B. Y., Nowicka, M., & Bartoszewicz, J. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.28.530492 · Cited by 1

A Large Financial Chat ModelPreprint

Ding, F. (2024) · doi:10.33774/coe-2024-lsc94

Reviewer #1 (Public Review): Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and Thermostability

Tan, Y., Zhou, B., Zheng, L., et al. (2024) · eLife · doi:10.7554/elife.98033.1.sa1

Reviewer #2 (Public Review): Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and Thermostability

Tan, Y., Zhou, B., Zheng, L., et al. (2024) · eLife · doi:10.7554/elife.98033.1.sa0

eLife Assessment: Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and Thermostability

Koo, P. K. (2024) · eLife · doi:10.7554/elife.98033.1.sa2

Protein Design

Ramya, L., Ragunathan, P. T., & Yennamalli, R. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00103-2

Novel Fine-Tuning Strategy on Pre-trained Protein Model Enhances ACP Functional Type Classification

Wang, S., & Ma, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5128-0_30

Protein Manufacture: Protein Design Assisted by Machine Learning from Backbone to Sequence

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Protein Family Sequence Generation through ProGen2 Fine-Tuning

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The Influence of Gendered Language on Leadership Decision-Making Styles

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Semantical and Geometrical Protein Encoding Toward Enhanced Bioactivity and ThermostabilityPreprint

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Protein Family Sequence Generation Through ProGen2 Fine-Tuning

Hrbáň, H., & Hoksza, D. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10947607

PLM-OMG: Protein Language Model-Based Ortholog Detection for Cross-Species Cell Type MappingPreprint

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

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Ranking Protein–Protein Models with Large Language Models and Graph Neural Networks

Xu, X., & Bonvin, A. M. J. J. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4623-6_4

Scaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins

Bhatnagar, A., Jain, S., Beazer, J., Curran, S. C., Hoffnagle, A. M., Ching, K., Martyn, M., Nayfach, S., Ruffolo, J. A., & Madani, A. (2025) · neural information processing systems · doi:10.52202/085713-1536 · Cited by 55

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Large language models generate functional protein sequences across diverse families

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Generation of protein sequences with a given characteristic

Hrbáň, H. (2024) · Digital Repository (National Repository of Grey Literature)