ArticleJournal of cheminformatics2021
GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Length-Adaptive LLM-Enhanced Method for Drug-Target Interaction Prediction.International journal of molecular sciences · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2025Article
- Psychological disorder detection: A multimodal approach using a transformer-based hybrid model.MethodsX · 2024Article
- A molecular video-derived foundation model for scientific drug discovery.Nature communications · 2024Article
- A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.PLoS computational biology · 2024Article
- DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning.Journal of cheminformatics · 2024Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Self- and cross-attention accurately predicts metabolite-protein interactions.NAR genomics and bioinformatics · 2023Article
- A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.Healthcare (Basel, Switzerland) · 2023Review
- In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2023Review
- GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47.Frontiers in chemistry · 2023Article
- Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors.BMC cancer · 2022Article
- Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development.Journal of cheminformatics · 2021Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
Traditional techniqueset identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation p to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug targrotocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drug-target interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through off-target binding, and repositioning opportunities.
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Registered trials
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