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Machine learning-driven molecular engineering of nucleic acids

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PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

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ATP-Pred: Prediction of Protein-ATP Binding Residues via Fusion of Residue-Level Embeddings and Kolmogorov–Arnold Network

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LassoESM a tailored language model for enhanced lasso peptide property prediction

Mi, X., Barrett, S. E., Mitchell, D. A., & Shukla, D. (2025) · Nature Communications · doi:10.1038/s41467-025-63412-3 · Cited by 7 · see citing works

Protein foundation models: a comprehensive survey

Xu, H., Li, L., Pan, S., Cheng, P., Wang, Y., Rong, Z., Feng, L., Huang, X., Wang, S., & Shu, W. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3147-2 · Cited by 6 · see citing works

MS-CoTF: Multi-scale chain-of-thought fusion for interpretable biological reasoning with large language models

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MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model

Hu, F., Zeng, W., & Peng, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf524 · Cited by 5 · see citing works

Protein language model identifies disordered, conserved motifs implicated in phase separation

Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309 · Cited by 4 · see citing works

Multi-feature enhanced protein language models for accurate protein-RNA binding residue prediction

Feng, Z., Yu, H., Guan, X., Gu, L., Li, K., & Zhou, X. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02065-7 · Cited by 4 · see citing works

PPAC: Predicting Protein–Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models

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TransABseq: A Two-Stage Approach for Predicting Antigen-Antibody Binding Affinity Changes upon Mutation Based on Protein Sequences

Li, C.-F., Yan, Z., Ge, F., Yu, X., Zhang, J., Zhang, M., & Yu, D.-J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00478 · Cited by 5 · see citing works

Advancing the accuracy of clathrin protein prediction through multi-source protein language models

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ESM-PsyPred: Leveraging Protein Language Models for Accurate Prediction of Psychrophilic Proteins

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Unveiling the landscape of prokaryotic global regulators through deep protein language models

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ClathPLM: Deep multi-view feature extraction with CNN and attention enhances clathrin protein identification

Song, S., Su, Y., Guo, Q., & Liu, T. (2026) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2026.109307 · Cited by 1 · see citing works

A Survey of Pretrained Protein Language Models

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Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning

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Improved multimodal protein language model-driven universal biomolecules-binding protein design with EiRAPreprint

Zeng, W., Zou, H., Li, X., Dou, Y., Wang, X., & Peng, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.02.673615 · Cited by 1 · see citing works

Protein Language Model Identifies Disordered, Conserved Motifs Implicated in Phase SeparationPreprint

Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309.2 · Cited by 1 · see citing works

Systematic Analysis of Stay-Green Genes in Six Ipomoea Species Reveals the Evolutionary Dynamics, Carotenoid and Anthocyanin Accumulation, and Stress Responses of Sweet Potato

Zuo, Z., Ma, H., Li, L., Qian, J., Zhang, M., Li, X., Sheng, Y., & Wang, Y. (2025) · Genes · doi:10.3390/genes16030266

Streamlining Protein Data Pre-processing Assisted by Machine Learning

Khan, M. U., & Abosaq, H. A. (2025) · Signals and communication technology · doi:10.1007/978-3-031-88634-8_15

MultiAlloDriver: a multi-model method to predict and identify cancer driver mutationsPreprint

Zhou, W., Song, Y., He, J., Li, M., Zhang, J., & Lu, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661496

PLM-VF: A novel predictor for virulent proteins in bacterial pathogens based on multi-scale CNN and protein language models

Guo, Q., Su, Y., & Liu, T. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.146075

Reviewer #1 (Public review): Protein language model identifies disordered, conserved motifs implicated in phase separation

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RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural Networks

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iDNA-DAPHA: a generic framework for methylation prediction via domain-adaptive pretraining and hierarchical attention

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Large models in drug design

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BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites

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ChaiSite: An Accurate Method for Protein–Nucleic Acid Binding Site Prediction Based on a Complex Structure Prediction Network

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Accurate Residue-Level Prediction of Linear Interacting Peptides with Auxiliary Multi-Scale Anchor Detection

Zhang, F., Xia, C., Dong, M., Zeng, M., Zhang, J., & Li, M. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356110

Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors

Su, Z., Zhang, X., & Tian, B. (2026) · iScience · doi:10.1016/j.isci.2026.115795

Unsupervised protein language models learn patterns of enzyme functionPreprint

Penner, M., Lihan, M., Bormke, H., Nix, P., Moscho, H., Dupree, P., & Hollfelder, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.23.720319

Hybrid deep learning model (E2BNet) combining ESM2 and BiLSTM for 8-state protein secondary structure prediction

Droba, D. D., Banu, A., Sami, M. I., Hossain, R., & Chowdhury, M. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08615-z

Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction

Basnet, S., & Cheng, J. J. (2026) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqag047

QSyncFold: quantum neural network for multidimensional sync-discovery in protein folding

Shi, J., Du, P., Zeng, W., Wang, W., Peng, S., & Li, X. F. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag234

DNA-binding specificity recognition from predicted homologous protein-DNA structuresPreprint

Zeng, W., Zou, H. X., Li, X., Liu, Y., Xu, L., Wang, X., & Peng, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732781

ESM2-BiMamba: A Length-Adaptive Hybrid Framework for Efficient Concurrent Prediction of DNA-Binding Proteins and DNA-Binding Residue Sites

Zhou, Y., Qiu, H., Liu, D., & Wang, W. M. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01246

AttentionNABP: Attention-Based Deep Learning Framework for Nucleic Acid-Binding Proteins Classification and Identification

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Machine Learning of Personal Repertoires From Public T Cell Receptors

Malca, O., Zilberberg, A., & Efroni, S. (2026) · Immunological Reviews · doi:10.1111/imr.70145

Interpretable prediction of nucleic acid-binding proteins using a protein language model

Kim, H., Lee, S., Oh, J., Kim, K. K., Kim, E., & Park, C. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag235

DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins

Phogat, A., Krishnan, S. R., Pandey, M., & Gromiha, M. M. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1920228

Semantic Integrity over Scale: A Complete-Semantic Framework for Measuring Corporate Digital Transformation

Fan, X., Li, Z., Zhang, Y., & Li, Z. (2026) · Journal of Data and Information Science · doi:10.1515/jdis-2026-0028

pLM-HP: Peptide hormone prediction using pre-trained protein language model representations

Ao, C., Jiao, S., Su, X., & Yang, H. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.10.002

PNBind : Prediction of Nucleic Acid‐Binding Sites Using Protein Structure and Protein Language Models

Li, Y., Dang, G., Shao, B., Li, P., & Liu, Z. (2026) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70190

PDI-Spot: A Robust Representation Learning Framework for Identifying Energetic Hotspots in Protein–DNA Complexes

Basaran, E., Gürsoy, A., Keskin, Ö., & Tunçbağ, N. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01579

DBP2Vec: Predicting DNA-binding proteins directly using pre-trained protein language modelPreprint

Wei, C., Ye, Z., Wang, P., & Lan, W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.30.502114 · Cited by 2 · see citing works

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks

Qi, D., Chen, S., & Liu, T. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115603 · Cited by 11 · see citing works

ESM2-BiMamba: A Length-Adaptive Hybrid Framework for Efficient Concurrent Prediction of DNA-Binding Proteins and DNA-Binding Residue Sites

Zhou, Y., Qiu, H., Liu, D., & Wang, W. M. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01246

PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models

Zhang, L., Li, X., Song, R., Song, Q., & Fan, X. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1763313

CNNCaps-DBP: Leveraging protein language models with attention-augmented convolution for DNA-binding protein prediction

Yan, Z., Geng, A., Li, Y., Wang, J., Xu, J., Yu, M., Wei, L., Zou, Q., Zhang, Z., & Cui, F. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108261 · Cited by 4 · see citing works