ReviewMolecular diversity2026
A comprehensive review of neural network-based approaches for drug-target interaction prediction.
Review in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting Drug-Target Interactions (DTI) is vital for accelerating drug discovery and repurposing. This review assesses the efficacy of neural network-based methods, including Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Transformer architectures utilizing self-attention mechanisms, in predicting DTI. Leveraging recent studies and benchmark datasets, such as DrugBank, neural networks outperform traditional methods, with hybrid architectures achieving an Area Under the Receiver-Operating Characteristic Curve (AUROC) of 0.979. These models enhance precision and reduce errors by integrating multimodal data, such as molecular graphs and protein sequences. However, challenges like high computational costs, limited interpretability, and data scarcity persist. Future advances may combine multimodal data with explainable Artificial Intelligence (AI) to improve DTI analysis and unlock new therapeutic opportunities. This study highlights the transformative potential of neural networks in computational drug discovery.
Indexed as
Identifiers
40715759What 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.