ArticleNature communications2025
Evidential deep learning-based drug-target interaction 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 21 papers.
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Who cites it
21 citing papers in PubMed.
- VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug-target hypotheses.Bioinformatics (Oxford, England) · 2026Article
- 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
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- A Node-Adaptive Feature Fusion Network for Drug-Target Interaction Prediction Based on Multi-View Graphs.Biomolecules · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- 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
- LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions.Journal of cheminformatics · 2026Article
- MSCMF-DTB: a multi-scale cross-modal fusion framework for drug-target binding prediction.Scientific reports · 2026Article
- Review
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- Vision transformer-based uncertainty quantification for triaging skin lesions: a probabilistic framework for automated biopsy recommendation.Frontiers in bioengineering and biotechnology · 2026Article
- Graph neural networks for multi-scale gene-drug interaction modeling in cancer: applications, challenges, and therapeutic opportunities for breast cancer drug resistance.Frontiers in oncology · 2026Review
- Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CS-DTA: a language model-driven framework for robust drug-target affinity prediction under strict cold-start scenarios.Frontiers in chemistry · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- An uncertainty-driven gated feature selection network (UGFS-Net) for TG level prediction: linking high-altitude exposure to lipid metabolism disorder via elevated TG.Lipids in health and disease · 2025Article
- Drug toxicity prediction based on genotype-phenotype differences between preclinical models and humans.EBioMedicine · 2025Article
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase modulators, uncertainty-guided predictions identify novel potential modulators targeting tyrosine kinase FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.
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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.