ArticleNature biomedical engineering2026
Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Polymeric Materials in Cancer Immunotherapy: Advances, Challenges, and Future Directions.Polymer science & technology (Washington, D.C.) · 2026Review
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- A generative framework for predicting cellular morphological and transcriptomic perturbation responses.Cell reports methods · 2026Article
- [Current status and challenges of artificial intelligence and organoid technologies in precision diagnosis and treatment of gastrointestinal stromal tumors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- ScalablebioRxiv : the preprint server for biology · 2026Article
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence in genomic medicine: dispelling three myths.NPJ genomic medicine · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- CONCERT predicts niche-aware perturbation responses in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Drug-tolerant persister cells in cancer: bridging the gaps between bench and bedside.Nature communications · 2025Review
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- scPRINT: pre-training on 50 million cells allows robust gene network predictions.Nature communications · 2025Article
- Decoding the mystery: AI-assisted bioinformatics and functional genomics technologies in medicinal plants.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations. In experiments in nine cell lines with chemical perturbations, PDGrapher identifies effective perturbagens in more testing samples than competing methods. It also shows competitive performance on ten genetic perturbation datasets. An advantage of PDGrapher is its direct prediction, in contrast to the indirect and computationally intensive approach common in phenotype-driven models. It trains up to 25× faster than existing methods, providing a fast approach for identifying therapeutic perturbations and advancing phenotype-driven drug discovery.
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