ReviewComputational and structural biotechnology journal2026
Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.
Review in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- From Patient Selection to Surveillance: Artificial Intelligence Applications in Radioiodine Therapy - A Systematic Review.Nuclear medicine and molecular imaging · 2026Review
- Alzheimer's disease-cancer research (inception to 2025): trends, themes, translational pathways, and insights from highly cited studies.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Adaptive Self-Attention Graph Pooling for Drug-Target Affinity Prediction.International journal of molecular sciences · 2026Article
- Article
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- MERS-Mpro Predictor: A Machine Learning-Based Tool for Rapid Screening of Potential MERS-CoV Main Protease Inhibitors.International journal of molecular sciences · 2026Article
- Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand-Protein Interactions.International journal of molecular sciences · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery is a long, resource-intensive process with high failure rates. Traditional experimental identification of drug-target interactions (DTIs) is especially time-consuming and costly. This comprehensive review examines how Artificial Intelligence (AI) and Machine Learning (ML) are transforming DTI prediction, offering substantial potential to reduce drug development time and costs. The review provides a detailed examination of AI/ML-based techniques, detailing data representations for drugs, targets, and their interactions through joint drug-target processing. The review extensively discusses feature extraction and engineering methods, including the construction of interaction-specific features. It also encompasses a broad range of learning paradigms and model architectures, including supervised learning, advanced Graph Neural Networks (GNNs), Deep Learning (DL) models, and hybrid approaches. We further examine training protocols and robust evaluation metrics crucial for assessing model generalization. Ultimately, this review highlights the capacity of these advanced AI/ML methods to deliver more accurate, scalable, and interpretable solutions for DTI prediction. This is crucial for accelerating key stages of drug development, including lead compound identification, off-target profiling, drug repurposing, polypharmacology analysis, and the realization of precision medicine.
Indexed as
Identifiers
What OpenQuestion holds
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.