ArticleNPJ digital medicine2024
CancerGPT for few shot drug pair synergy prediction using large pretrained language models.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Review
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Hyper-RAG: combating LLM hallucinations using hypergraph-driven retrieval-augmented generation.Nature communications · 2026Article
- DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- A comprehensive review of intelligent question-answering systems in traditional Chinese medicine based on LLMs.Journal of pharmaceutical analysis · 2026Review
- Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prexdiction with Multimodal Biomedical Data.ArXiv · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- CRISPR-GPT for agentic automation of gene-editing experiments.Nature biomedical engineering · 2026Article
- Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- FA-DeepMSM: a few-shot adapted interpretable multimodal survival model for improved prognostic prediction in glioblastoma.Scientific reports · 2026Article
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. Our experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples. Our proposed model, the CancerGPT (with ~ 124M parameters), is comparable to the larger fine-tuned GPT-3 model (with ~ 175B parameters). Our research contributes to tackling drug pair synergy prediction in rare tissues with limited data, and also advancing the use of LLMs for biological and medical inference tasks.
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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.