ArticleBMC bioinformatics2022
Using BERT to identify drug-target interactions from whole PubMed.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Classification of health product defect reports by deep learning.Scientific reports · 2026Article
- Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH).Communications chemistry · 2026Article
- Bridging the computational-experimental gap: leveraging large language model to prioritize Alzheimer's therapeutics based on comparison of learning models.npj health systems · 2026Article
- Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Advances and challenges in drug repurposing in precision therapeutics of colorectal cancer.World journal of gastrointestinal oncology · 2025Review
- Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based classification models.Bioinformatics advances · 2025Article
- Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.Database : the journal of biological databases and curation · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model.Bioinformatics advances · 2024Article
- Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies.JCO clinical cancer informatics · 2023Review
- Nano-Zirconium Dioxide Catalyzed Multicomponent Synthesis of Bioactive Pyranopyrazoles That Target Cyclin Dependent Kinase 1 in Human Breast Cancer Cells.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundDrug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format.
resultsOur novel method identified 0.6 million articles (along with drug and protein information) which are not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~ 99% accuracy for identifying articles containing quantitative drug-target profiles. The F1 micro for the prediction of assay format is 88%, which leaves room for improvement in future studies.
conclusionThe BERT model in this study is robust and the proposed pipeline can be used to identify previously overlooked articles containing quantitative DTIs. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.