ArticleNature communications2025
scDrugMap: benchmarking large foundation models for drug response prediction.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- TMEDRP: decoding tumor-intrinsic and microenvironmental signatures for clinical drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.Briefings in bioinformatics · 2026Review
- ModelistsGCN: a multimodal graph convolutional network framework for single-cell spatial transcriptomic cell typing.Briefings in bioinformatics · 2026Article
- A multi-database pharmacovigilance study reveals distinctive immunosuppressive and opportunistic infection disproportionality signals with bevacizumab and temozolomide combination therapy in glioblastoma.Frontiers in medicine · 2026Article
- scDrugMap: benchmarking large foundation models for drug response prediction.Nature communications · 2025Article
- Large Language Models for Translational Cancer Informatics.JCO clinical cancer informatics · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Drug resistance remains a major challenge in cancer treatment. While single-cell profiling offers unprecedented resolution for uncovercovering resistance mechanisms, the potential of emerging foundation models for drug response prediction at the single-cell level is still largely unknown. Here, we introduce scDrugMap, a unified framework featuring both Python toolkits and an interactive web server for benchmarking and predicting drug responses with state-of-the-art foundation models. scDrugMap evaluates eight single-cell foundation models and two large language models across 495,000 cells from 60 datasets, spanning diverse tissues, drugs, cancer types, and treatment conditions. In pooled-data evaluation, scFoundation delivered the strongest performance, particularly in tumor tissue. In cross-data analysis, UCE performed best after fine-tuning, while in zero-shot settings, scGPT achieved the highest accuracy. Together, scDrugMap provides the first systematic benchmark of foundation models for single-cell drug response prediction and offers a powerful, user-friendly platform to accelerate drug discovery and translational precision oncology.
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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.