ArticleInterdisciplinary sciences, computational life sciences2025
Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.
Article in Interdisciplinary sciences, computational life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- DRQuantum: a drug repurposing method by quantum walks on a multi-layered heterogeneous network.Scientific reports · 2026Article
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- LLMs uncover 6M1@C60's therapeutic targets: inhibiting atherosclerotic progression via NRF2/HO-1/GPX4 axis-mediated ferroptosis suppression.Journal of nanobiotechnology · 2026Article
- Generative AI in drug repurposing and biomarker discovery: a multimodal approach.Frontiers in bioinformatics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Large Language Model-Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study.JMIR medical informatics · 2025Article
- DerivaPredict: A User-Friendly Tool for Predicting and Evaluating Active Derivatives of Natural Products.Molecules (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
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Abstract
Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDA
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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.