ArticleInternational journal of molecular sciences2022
DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 50 citations in OpenAlex.
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- Graph neural network-based mutation-aware regression test ordering using code dependency graphs and execution traces.MethodsX · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- AI-powered IC50 prediction for p53 inhibitors drug-target interaction via hybrid graph neural networks.Journal of computer-aided molecular design · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Fast-adapting graph neural network with prior knowledge for drug response prediction across preclinical and clinical data.Journal of pharmaceutical analysis · 2025Article
- Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy.Biomolecules · 2025Review
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion.BMC biology · 2024Article
- PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation.Briefings in bioinformatics · 2024Article
- Article
- Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- Tracing unknown tumor origins with a biological-pathway-based transformer model.Cell reports methods · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.
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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.