{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T09:01:55Z","timestamp":1789635715347,"version":"4.0.0"},"reference-count":28,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:00:00Z","timestamp":1789171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Drug\u2013target affinity (DTA) prediction plays an important role in computational drug discovery; however, its application to emerging infectious diseases such as Ebola remains challenging because of the limited availability of experimentally measured affinity data. To address this low-resource setting, we propose a retrieval-guided transfer-learning framework that leverages BindingDB interactions to improve Ebola DTA prediction. The framework uses a two-stage strategy. In Stage I, source interactions are selected using random sampling, compound-similarity retrieval, protein-similarity retrieval, or hybrid compound\u2013protein retrieval at source-data budgets of 50,000 and 300,000 interactions and combined with Ebola training data to learn transferable representations. In Stage II, the pretrained compound and protein encoders are frozen, while the prediction layers are adapted to the Ebola domain. The framework was implemented with DeepDTA and GraphDTA and evaluated across five random seeds using a scaffold-based split, with conventional machine-learning models and single-stage deep learning as baselines. The best overall configuration, GraphDTA with protein-similarity-guided retrieval at the 50,000-interaction budget, achieved RMSE =0.5498\u00b10.1073, R2=0.8809\u00b10.0452, and Pearson =0.9395\u00b10.0237, outperforming the strongest conventional machine-learning model (Extra Trees, RMSE =0.6065\u00b10.0642) and single-stage GraphDTA (RMSE =0.6533\u00b10.0752). Across both source-data budgets and both DTA backbones, all targeted retrieval configurations achieved lower mean RMSE than their corresponding random-retrieval configurations. At the 50,000-interaction budget, matched seed-wise analysis further showed consistent improvements for protein-guided retrieval across all five seeds for both backbones. Retrieval characterization showed stronger target-domain similarity and substantially lower cross-strategy overlap at 50,000 than at 300,000 interactions, while post hoc sequence analysis independently confirmed enrichment of sequence-level relatedness with protein-guided retrieval. Increasing the source-data budget from 50,000 to 300,000 did not uniformly improve predictive performance, indicating that source-data relevance and retrieval selectivity should be considered jointly with source-data quantity. Finally, virtual-screening and approved-drug repurposing case studies across six Ebola virus targets demonstrate the use of the framework for computational prioritization of compound\u2013target hypotheses. Overall, the findings support relevance-guided source-data selection as an effective strategy for transfer learning in low-resource DTA prediction.<\/jats:p>","DOI":"10.3390\/ijms27188147","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:09:51Z","timestamp":1789387791000},"page":"8147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Retrieval-Guided Transfer Learning for Low-Resource Ebola Drug\u2013Target Affinity Prediction"],"prefix":"10.3390","volume":"27","author":[{"given":"Mubarakah","family":"Alotaibi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-7843","authenticated-orcid":false,"given":"Nada Al","family":"Taweraqi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s40121-023-00913-y","article-title":"Navigating the Complex Landscape of Ebola Infection Treatment: A Review of Emerging Pharmacological Approaches","volume":"13","author":"Pinto","year":"2024","journal-title":"Infect. Dis. Ther."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4571","DOI":"10.1039\/D5MD00533G","article-title":"Small molecule drug discovery for Ebola virus disease","volume":"16","author":"Durante","year":"2025","journal-title":"RSC Med. Chem."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kwofie, S.K., Adams, J., Broni, E., Enninful, K.S., Agoni, C., Soliman, M.E., and Wilson, M.D. (2023). Artificial Intelligence, machine learning, and big data for Ebola virus drug discovery. Pharmaceuticals, 16.","DOI":"10.3390\/ph16030332"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e202500348","DOI":"10.1002\/open.202500348","article-title":"Drug Repurposing Investigation for Combating Ebola Virus Disease: Database Mining, Docking Calculations, Molecular Dynamics, and Density Functional Theory Study","volume":"14","author":"Abdelrahman","year":"2025","journal-title":"ChemistryOpen"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: Deep drug\u2013target binding affinity prediction","volume":"34","author":"Ozkirimli","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"GraphDTA: Predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"D1633","DOI":"10.1093\/nar\/gkae1075","article-title":"BindingDB in 2024: A FAIR knowledgebase of protein-small molecule binding data","volume":"53","author":"Liu","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, T., Zhang, H., Parashar, S., and Kong, S. (2025, January 11\u201315). Few-shot recognition via stage-wise retrieval-augmented finetuning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01405"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107766","DOI":"10.1016\/j.compbiolchem.2022.107766","article-title":"EBOLApred: A machine learning-based web application for predicting cell entry inhibitors of the Ebola virus","volume":"101","author":"Adams","year":"2022","journal-title":"Comput. Biol. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104908","DOI":"10.1016\/j.antiviral.2020.104908","article-title":"Repurposing Pyramax\u00ae, quinacrine and tilorone as treatments for Ebola virus disease","volume":"182","author":"Lane","year":"2020","journal-title":"Antivir. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hayat, M., Gao, T., Cao, Y., Rafiq, M., Zhuo, L., and Li, Y.Z. (2024). Identification of prospective Ebola virus VP35 and VP40 protein inhibitors from myxobacterial natural products. Biomolecules, 14.","DOI":"10.3390\/biom14060660"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Broni, E., Ashley, C., Adams, J., Manu, H., Aikins, E., Okom, M., Miller, W.A.I., Wilson, M.D., and Kwofie, S.K. (2023). Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24076298"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102636","DOI":"10.1016\/j.jiph.2024.102636","article-title":"Discovery of anti-Ebola virus multi-target inhibitors from traditional Chinese medicine database using molecular screening, biophysical investigation, and binding free energy calculations","volume":"18","author":"Khan","year":"2025","journal-title":"J. Infect. Public Health"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7416","DOI":"10.1038\/s41598-024-57879-1","article-title":"GEFormerDTA: Drug target affinity prediction based on transformer graph for early fusion","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qi, H., Yu, T., Yu, W., and Liu, C. (2024). Drug\u2013target affinity prediction with extended graph learning-convolutional networks. BMC Bioinform., 25.","DOI":"10.1186\/s12859-024-05698-6"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"btaf002","DOI":"10.1093\/bioinformatics\/btaf002","article-title":"Improving generalizability of drug\u2013target binding prediction by pre-trained multi-view molecular representations","volume":"41","author":"Ouyang","year":"2025","journal-title":"Bioinformatics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"D198","DOI":"10.1093\/nar\/gkl999","article-title":"BindingDB: A web-accessible database of experimentally determined protein\u2013ligand binding affinities","volume":"35","author":"Liu","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"bbad189","DOI":"10.1093\/bib\/bbad189","article-title":"MPI-VGAE: Protein\u2013metabolite enzymatic reaction link learning by variational graph autoencoders","volume":"24","author":"Wang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"ref_19","unstructured":"Dalk\u0131ran, A. (2024). Drug-Target Interaction Prediction by Transfer Learning for Proteins with Few Bioactive Compund Data. [Ph.D. Thesis, Middle East Technical University (Turkey)]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1038\/nbt.3988","article-title":"MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets","volume":"35","author":"Steinegger","year":"2017","journal-title":"Nat. Biotechnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"190ra79","DOI":"10.1126\/scitranslmed.3005471","article-title":"FDA-Approved Selective Estrogen Receptor Modulators Inhibit Ebola Virus Infection","volume":"5","author":"Johansen","year":"2013","journal-title":"Sci. Transl. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"290ra89","DOI":"10.1126\/scitranslmed.aaa5597","article-title":"A Screen of Approved Drugs and Molecular Probes Identifies Therapeutics with Anti-Ebola Virus Activity","volume":"7","author":"Johansen","year":"2015","journal-title":"Sci. Transl. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e01711-17","DOI":"10.1128\/AAC.01711-17","article-title":"Efficacy of Tilorone Dihydrochloride against Ebola Virus Infection","volume":"62","author":"Ekins","year":"2018","journal-title":"Antimicrob. Agents Chemother."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL Database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"D1373","DOI":"10.1093\/nar\/gkac956","article-title":"PubChem 2023 update","volume":"51","author":"Kim","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3186","DOI":"10.1021\/jm401411z","article-title":"Molecular similarity in medicinal chemistry: Miniperspective","volume":"57","author":"Maggiora","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3.1.1","DOI":"10.1002\/0471250953.bi0301s42","article-title":"An introduction to sequence similarity (\u201chomology\u201d) searching","volume":"42","author":"Pearson","year":"2013","journal-title":"Curr. Protoc. Bioinform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1006\/jmbi.2000.3550","article-title":"Assessing annotation transfer for genomics: Quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores","volume":"297","author":"Wilson","year":"2000","journal-title":"J. Mol. Biol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/18\/8147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:05:45Z","timestamp":1789632345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/18\/8147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,12]]},"references-count":28,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["ijms27188147"],"URL":"https:\/\/doi.org\/10.3390\/ijms27188147","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,12]]}}}