ArticleAging2023
Biomedical generative pre-trained based transformer language model for age-related disease target discovery.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- LLMs uncover 6M1@C60's therapeutic targets: inhibiting atherosclerotic progression via NRF2/HO-1/GPX4 axis-mediated ferroptosis suppression.Journal of nanobiotechnology · 2026Article
- Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking system.Scientific reports · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Advances in anti-aging Drug research leveraging multi-omics and artificial intelligence.Frontiers in aging · 2026Review
- Evaluation of (Z)-endoxifen as a potential therapy for glioblastoma multiforme through computational and experimental analyses.Scientific reports · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
- Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives.Journal of medical Internet research · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Complement-ARIE: Catalyzing the Development and Adoption of New Approach Methodologies.NAM journal · 2025Article
- Efficacy of large language models and their potential in Obstetrics and Gynecology education.Obstetrics & gynecology science · 2024Article
- Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.
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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.