ArticleBMC biology2024
DrugReAlign: a multisource prompt framework for drug repurposing based on large language models.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- DrugReasoner: Interpretable drug approval prediction with a reasoning-augmented language model.PloS one · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- AI-Driven Drug Discovery: A Comprehensive Review.ACS omega · 2025Review
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Article
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- IRGL-RRI: interpretable graph representation learning for plant RNA-RNA interaction discovery.Frontiers in plant science · 2025Article
- Computational discovery of natural medicines targeting adenosine receptors for metabolic diseases.Frontiers in pharmacology · 2025Article
- Improving drug-drug interaction prediction via in-context learning and judging with large language models.Frontiers in pharmacology · 2025Article
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
- Integrating large language models into clinical pharmacy education: applications in perioperative medication management for gastric cancer.Frontiers in medicine · 2025Article
- Utilizing large language models for gastroenterology research: a conceptual framework.Therapeutic advances in gastroenterology · 2025Review
- Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery.Frontiers in genetics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.
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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.