ArticleNature communications2025
GraphBAN: An inductive graph-based approach for enhanced prediction of compound-protein interactions.
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 31 papers.
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Who cites it
31 citing papers in PubMed.
- From Glycan Biology to Drug Candidates: An Integrated Sialylation Niche Index and AI-Guided Therapeutic Framework for Head and Neck Squamous Cell Carcinoma.Biomedicines · 2026Article
- Dynamic CD4+ T Cell Genetics Identifies Candidate Therapeutic Targets for Allergic Rhinitis: Mendelian Randomization with Exploratory Single-Cell Contextualization.Biomedicines · 2026Article
- Emerging natural product development strategies drive the therapeutic potential exploration and clinical translation of hydroxysafflor yellow A.Journal of pharmaceutical analysis · 2026Review
- Resnet-Driven In Silico Identification of Lead Peptides from the Venom Gland Transcriptome ofPharmaceuticals (Basel, Switzerland) · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- Integrating transcriptomics, network pharmacology, and GraphBAN neural networks to identify biomarkers and regulatory mechanisms of classical traditional Chinese medicine formulations in ulcerative colitis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.Acta pharmaceutica Sinica. B · 2026Article
- CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.BioData mining · 2026Article
- ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.bioRxiv : the preprint server for biology · 2026Article
- HKD-CPI: high-order knowledge distillation enhanced inductive compound-protein interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- PFAS is associated with perineural invasion in triple-negative breast cancer with a potential role for Cathepsin D dysregulation: a multi-omics and experimental study.Clinical and experimental medicine · 2026Article
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- Targeting the OXNAD1-PTGS2 axis with resveratrol overcomes ferroptosis Inhibition and reverses 5-FU resistance in gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- GraphBAN-based identification of active ingredients and key gene targets in compound Chinese herbal medicines for polycystic ovary syndrome.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Identification of key bioactive compounds of medicine-food homologous substances and their multi-target intervention effects in ulcerative colitis treatment.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- SynerDTI: a synergistic deep learning framework for drug-target interaction prediction via global feature coordinated attention mechanism.Molecular diversity · 2026Article
- Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients.NPJ precision oncology · 2026Article
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Authors and funding
8 authors.
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Abstract
Understanding compound-protein interactions is crucial for early drug discovery, offering insights into molecular mechanisms and potential therapeutic effects of compounds. Here, we introduce GraphBAN, a graph-based framework that inductively predicts these interactions using compound and protein feature information. GraphBAN effectively handles inductive link predictions for unseen nodes, providing a robust solution for predicting interactions between entirely unseen compounds and proteins. This capability enables GraphBAN to transcend the constraints of traditional methods that are typically limited to known contexts. GraphBAN employs a knowledge distillation architecture through a teacher-student learning model. The teacher block leverages network structure information, while the student block focuses on node attributes, enhancing learning and prediction accuracy. Additionally, GraphBAN incorporates a domain adaptation module, increasing its effectiveness across different dataset domains. Empirical tests on five benchmark datasets demonstrate that GraphBAN outperforms ten baseline models, while a case study analysis with the Pin1 protein further supports the model's effectiveness in real world scenarios, making it as a promising tool for early drug discovery.
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