ReviewMolecules (Basel, Switzerland)2019
Revealing Drug-Target Interactions with Computational Models and Algorithms.
Review in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Artificial intelligence-driven digital twins in Pharma 4.0: transforming smart manufacturing, predictive quality assurance, and personalized drug delivery.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Domain-generalized representation learning for cross-chemical-family toxicity prediction.Scientific reports · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- A Node-Adaptive Feature Fusion Network for Drug-Target Interaction Prediction Based on Multi-View Graphs.Biomolecules · 2026Article
- CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.BioData mining · 2026Article
- Predictive modeling of influenza strain drugs using temperature-based topological indices and regression analysis via multi-criteria decision making techniques.Scientific reports · 2026Article
- An integrated evolutionary optimization and machine learning framework for predicting pharmacokinetics of nanocarrier-encapsulated drugs.Frontiers in medicine · 2026Article
- Identification and validation of druggable targets for cataract using mendelian randomization: functional insights from multi-omics and an oxidative stress model.Frontiers in medicine · 2026Article
- From structural insight to molecule: Integrative molecular simulations identify candidate pyruvate kinase activators.PloS one · 2026Article
- Virulence arresting drug discovery by strategies targeting bacterial virulence: Mainly focusing on quorum-sensing interference and biofilm inhibition.Journal of pharmaceutical analysis · 2025Review
- Multi criterion decision making analysis of hematologic cancer drugs via topological indices and physicochemical properties.Scientific reports · 2025Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
- Investigation of Potential Inhibitors of N-Myristoyltransferase in Leishmania amazonensis: A Computational and Experimental Study.Chemical biology & drug design · 2025Article
- Differential Inhibition by Cenobamate of Canonical Human Nav1.5 Ion Channels and Several Point Mutants.International journal of molecular sciences · 2025Article
- Prediction of drug target interaction based on under sampling strategy and random forest algorithm.PloS one · 2025Article
- Best practices for the collection and analysis of patient experience data from social media for patient-focused drug development.Frontiers in medicine · 2025Review
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening.Health information science and systems · 2023Article
- MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction.BMC bioinformatics · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundIdentifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates.
methodsWe introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning.
resultsAlthough computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
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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.