ReviewFrontiers in genetics2021
Application of Machine Learning for Drug-Target Interaction Prediction.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 63 citations in OpenAlex.
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Anti-angiogenesis effect of Baiying Juhua Decoction on the non-small cell lung cancer: integrating pharmacology, multi-machine learning and experimental investigation.Bioresources and bioprocessing · 2026Article
- Transformer Learning in Sequence-Based Drug Design Depends on Compound Memorization and Similarity of Sequence-Compound Pairs.Molecular informatics · 2026Review
- AI-powered IC50 prediction for p53 inhibitors drug-target interaction via hybrid graph neural networks.Journal of computer-aided molecular design · 2025Article
- Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.Nature machine intelligence · 2025Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.BMC bioinformatics · 2025Article
- Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.Journal of chemical information and modeling · 2025Article
- Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.Pharmacy (Basel, Switzerland) · 2025Article
- Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins.Scientific reports · 2025Article
- Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.Chinese medical journal · 2025Article
- Mechanisms to medicines: navigating drug repurposing strategies in Alzheimer's disease.Frontiers in aging neuroscience · 2025Review
- Intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning.World journal of clinical cases · 2024Article
- Article
- Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.BMC biology · 2024Article
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exploring drug-target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug-target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug-target interaction prediction research. In this review, details of the specific applications of machine learning in drug-target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.
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.