ReviewThe AAPS journal2017
Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review.
Review in The AAPS journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- The dual GLP-1 and GIP receptor agonist tirzapetide provides an unintended interaction with the β-adrenoceptors and plays a role in glucose metabolism in hyperglycemic or senescent cardiac cells.Cardiovascular diabetology · 2025Article
- Factor-specific generative pattern from large-scale drug-induced gene expression profile.Scientific reports · 2023Article
- A Novel Deep Neural Network Technique for Drug-Target Interaction.Pharmaceutics · 2022Article
- Sequence-based prediction of protein binding regions and drug-target interactions.Journal of cheminformatics · 2022Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Elucidating direct kinase targets of compound Danshen dropping pills employing archived data and prediction models.Scientific reports · 2021Article
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.International journal of molecular sciences · 2020Article
- Drug-target interactions prediction using marginalized denoising model on heterogeneous networks.BMC bioinformatics · 2020Article
- Validation strategies for target prediction methods.Briefings in bioinformatics · 2020Review
- Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?Journal of molecular modeling · 2020Review
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Gene Expression Signature-Based Approach Identifies Antifungal Drug Ciclopirox As a Novel Inhibitor of HMGA2 in Colorectal Cancer.Biomolecules · 2019Article
- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
- Revealing Drug-Target Interactions with Computational Models and Algorithms.Molecules (Basel, Switzerland) · 2019Review
- Review
- A new chemoinformatics approach with improved strategies for effective predictions of potential drugs.Journal of cheminformatics · 2018Article
- Formononetin induces vasorelaxation in rat thoracic aorta via regulation of the PI3K/PTEN/Akt signaling pathway.Drug design, development and therapy · 2018Article
- VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.BMC bioinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.
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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.