{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T05:04:10Z","timestamp":1784005450142,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2023YFC2206400"],"award-info":[{"award-number":["2023YFC2206400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Drug\u2013target interaction (DTI) prediction is central to drug repurposing. Although existing methods integrate multi-dimensional features and have achieved certain progress, they rely heavily on molecular structure or graph structure information. This makes it difficult to capture complex biomedical semantic associations, leading to insufficient semantic information. To address this issue, this paper proposes BioLA-DTI, a large language model (LLM)-enhanced method for drug\u2013target interaction prediction. The method leverages LLMs to provide semantic information for drugs and proteins. Furthermore, existing methods apply uniform weighting strategies to proteins with significantly different lengths. This introduces noise for short proteins and under-represents long ones. To tackle the feature imbalance problem caused by protein-length variation, this work designs a length-adaptive fusion module. This module dynamically weights the LLM-derived semantic features against structural features based on the actual protein-sequence length. Experiments on two benchmark datasets show that BioLA-DTI outperforms existing baseline methods across multiple evaluation metrics, particularly in cold-start scenarios. Case studies demonstrate that this model can provide valuable references for drug discovery. Ablation studies further confirm the critical roles of LLM enhancement and length adaptation modules.<\/jats:p>","DOI":"10.3390\/ijms27146212","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:53:58Z","timestamp":1783932838000},"page":"6212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Length-Adaptive LLM-Enhanced Method for Drug\u2013Target Interaction Prediction"],"prefix":"10.3390","volume":"27","author":[{"given":"Hengli","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, No. 100, Science Avenue, Zhengzhou 450001, China"},{"name":"National Supercomputing Center in Zhengzhou, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Villoutreix, B.O., Poyet, J.L., and Tsaioun, K. (2024). Editorial: Drug discovery and development explained: Introductory notes for the general public. Front. Drug Discov., 4.","DOI":"10.3389\/fddsv.2024.1528467"},{"key":"ref_2","first-page":"10101184","article-title":"In silico methods for drug-target interaction prediction","volume":"5","author":"Ru","year":"2025","journal-title":"Cell Rep. Methods"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","article-title":"MolTrans: Molecular Interaction Transformer for drug-target interaction prediction","volume":"37","author":"Huang","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: Deep drug-target binding affinity prediction","volume":"34","author":"Ozturk","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1021\/acs.jcim.4c02088","article-title":"DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism","volume":"65","author":"Peng","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e70183","DOI":"10.1111\/cbdd.70183","article-title":"Deep Learning for Drug-Target Interaction Prediction: A Comprehensive Review","volume":"106","author":"Chen","year":"2025","journal-title":"Chem. Biol. Drug Des."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"61048","DOI":"10.1021\/acsomega.5c04997","article-title":"A Systematic Review of Drug-Related Interactions-Utilizing Deep Learning and LLMs for Prediction and Mitigation","volume":"10","author":"Behrooznia","year":"2025","journal-title":"ACS Omega"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13187","DOI":"10.1021\/acs.jcim.5c02283","article-title":"BioFusionDTI: Assimilating Graph and Sequence Modalities for Generalizable Drug-Target Interaction Prediction","volume":"65","author":"Chen","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/TNB.2024.3441590","article-title":"TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks","volume":"23","author":"Tang","year":"2024","journal-title":"IEEE Trans. Nanobiosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1039\/D1SC05180F","article-title":"MGraphDTA: Deep multiscale graph neural network for explainable drug-target binding affinity prediction","volume":"13","author":"Yang","year":"2022","journal-title":"Chem. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Feng, Y., Yang, X., Guan, Y., Zhang, J., Yang, H., Wang, Z., and Cheng, Q. (2025). KG-MACNF: A nonlinear cross-modal fusion model for predicting drug-target interactions via multi-relational embedding and fine-grained structure. PLoS ONE, 20.","DOI":"10.2139\/ssrn.5127482"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qian, Y., Li, X., Wu, J., and Zhang, Q. (2023). MCL-DTI: Using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction. BMC Bioinform., 24.","DOI":"10.1186\/s12859-023-05447-1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1146\/annurev.biochem.74.082803.133029","article-title":"Protein Families and Their Evolution-A Structural Perspective","volume":"74","author":"Orengo","year":"2005","journal-title":"Annu. Rev. Biochem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.physa.2004.11.021","article-title":"Length, Protein\u2013Protein Interactions, and Complexity","volume":"350","author":"Tan","year":"2005","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1609\/aaai.v38i1.27784","article-title":"PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction","volume":"Volume 38","author":"Wu","year":"2024","journal-title":"Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, G., and Ignatova, Z. (2009). Generic Algorithm to Predict the Speed of Translational Elongation: Implications for Protein Biogenesis. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0005036"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Traut, T. (2014). Multidomain Proteins. Encyclopedia of Life Sciences, John Wiley & Sons, Ltd.","DOI":"10.1002\/9780470015902.a0005053.pub2"},{"key":"ref_19","unstructured":"Giudice, J. (2025). Structure\u2013Activity Relationships in the Endosomal Escape of Designed Miniature Proteins. ProQuest Dissertations and Theses Global, University of California, Berkeley."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vazzana, G., Manfredi, M., Savojardo, C., Martelli, P.L., and Casadio, R. (2026). Embedding-Based Alignments Capture Structural and Sequence Domains of Distantly Related Multifunctional Human Proteins. Computation, 14.","DOI":"10.3390\/computation14010025"},{"key":"ref_22","unstructured":"Xu, M., Yuan, X., Miret, S., and Tang, J. (2023). ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1109\/TCBBIO.2025.3541634","article-title":"LLMDTA: Improving Cold-Start Prediction in Drug-Target Affinity with Biological LLM","volume":"22","author":"Tang","year":"2025","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: Generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3458754","article-title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","volume":"3","author":"Gu","year":"2022","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lee, I., Keum, J., and Nam, H. (2019). DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007129"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"TransformerCPI: Improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s13321-021-00540-0","article-title":"GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data","volume":"13","author":"Liu","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"bbad082","DOI":"10.1093\/bib\/bbad082","article-title":"MCANet: Shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction","volume":"24","author":"Bian","year":"2023","journal-title":"Brief. Bioinform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s42256-022-00605-1","article-title":"Interpretable bilinear attention network with domain adaptation improves drug-target prediction","volume":"5","author":"Bai","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1021\/acs.jcim.4c00957","article-title":"MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism","volume":"64","author":"Peng","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108449","DOI":"10.1016\/j.compbiolchem.2025.108449","article-title":"MGMA-DTI: Drug target interaction prediction using multi-order gated convolution and multi-attention fusion","volume":"118","author":"Li","year":"2025","journal-title":"Comput. Biol. Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.1111\/bph.16325","article-title":"The dark sides of the GPCR tree\u2014Research progress on understudied GPCRs","volume":"182","author":"Scharf","year":"2025","journal-title":"Br. J. Pharmacol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Torrisi, M., de la Vega de Le\u00f3n, A., Climent, G., Loos, R., and Panjkovich, A. (2022). Improving the Assessment of Deep Learning Models in the Context of Drug-Target Interaction Prediction. bioRxiv, preprint.","DOI":"10.1101\/2022.04.20.488898"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","article-title":"HyperAttentionDTI: Improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism","volume":"38","author":"Zhao","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5545","DOI":"10.1093\/bioinformatics\/btaa1005","article-title":"DeepPurpose: A deep learning library for drug-target interaction prediction","volume":"36","author":"Huang","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_37","unstructured":"Xu, T. (2025, January 24\u201328). Towards a Complete Logical Framework for GNN Expressiveness. Proceedings of the 13th International Conference on Learning Representations (ICLR 2025), Singapore."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xu, X., Jiang, W., Liu, W., Liu, B., and Wang, Y. (2024). Graph Unseen Node Embedding Learning Network for Drug Target Affinity Prediction. Proceedings of the 2024 IEEE 17th International Conference on Signal Processing (ICSP), IEEE.","DOI":"10.1109\/ICSP62129.2024.10846652"},{"key":"ref_39","unstructured":"Yoo, I., Bi, J., and Hu, X. (2019). GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 18\u201321 November 2019, IEEE. NSF, IEEE International Conference on Bioinformatics and Biomedicine-BIBM."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"btae135","DOI":"10.1093\/bioinformatics\/btae135","article-title":"iNGNN-DTI: Prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models","volume":"40","author":"Sun","year":"2024","journal-title":"Bioinformatics"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, J., Sun, L., Liu, L., and Li, Z. (2024). MIFAM-DTI: A drug-target interactions predicting model based on multi-source information fusion and attention mechanism. Front. Genet., 15.","DOI":"10.3389\/fgene.2024.1381997"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10713","DOI":"10.1021\/acs.jcim.5c01429","article-title":"Cross-Modal Interaction-Aware Progressive Fusion Network for Drug-Target Interaction Prediction","volume":"65","author":"Cao","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"ref_43","unstructured":"Zitnik, M., Sosic, R., and Leskovec, J. (2026, March 05). BioSNAP Datasets: Stanford Biomedical Network Dataset Collection. Available online: http:\/\/snap.stanford.edu\/biodata."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"D198","DOI":"10.1093\/nar\/gkl999","article-title":"BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities","volume":"35","author":"Liu","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"vbaf306","DOI":"10.1093\/bioadv\/vbaf306","article-title":"DCGAT-DTI: Dynamic cross-graph attention network for drug-target interaction prediction","volume":"6","author":"Abir","year":"2026","journal-title":"Bioinform. Adv."},{"key":"ref_46","unstructured":"Leng, Z., Tan, M., Liu, C., Cubuk, E.D., Shi, X., Cheng, S., and Anguelov, D. (2022). PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Huang, Y., Qi, J., Wang, X., and Lin, Z. (2023). Asymmetric Polynomial Loss for Multi-Label Classification. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP49357.2023.10095437"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nevers, Y., Glover, N., Dessimoz, C., and Lecompte, O. (2021). Protein length distribution is remarkably consistent across Life. bioRxiv, preprint, later published in Genome Biology.","DOI":"10.1101\/2021.12.03.470944"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/14\/6212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:15:32Z","timestamp":1784002532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/14\/6212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,12]]},"references-count":49,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["ijms27146212"],"URL":"https:\/\/doi.org\/10.3390\/ijms27146212","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,12]]}}}