ReviewCell reports methods2025
In silico methods for drug-target interaction prediction.
Review in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed.
- Deep Learning Protocols for Predicting Drug Mechanism of Action and Drug-Target Interactions.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Article
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- A Length-Adaptive LLM-Enhanced Method for Drug-Target Interaction Prediction.International journal of molecular sciences · 2026Article
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- A Node-Adaptive Feature Fusion Network for Drug-Target Interaction Prediction Based on Multi-View Graphs.Biomolecules · 2026Article
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
- In silico pharmacological analysis of Tinospora cordifolia compounds targeting African swine fever virus B175L.PloS one · 2026Article
- DTBind: A Mechanism-Driven Deep Learning Framework for Accurate Prediction of Drug-Target Molecular Recognition.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the "guilt-by-association" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.
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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.