ArticleFrontiers in chemistry2016
How Reliable Are Ligand-Centric Methods for Target Fishing?
Article in Frontiers in chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- Enhancing the confidence of potential targets enriched by similarity-centric models: the crucial role of the similarity threshold.Frontiers in pharmacology · 2025Article
- Target fishing: from "needle in haystack" to "precise guidance"--new technology, new strategy and new opportunity.Frontiers in pharmacology · 2025Review
- Benchmarking reverse docking through AlphaFold2 human proteome.Protein science : a publication of the Protein Society · 2024Article
- Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning.Communications chemistry · 2024Article
- Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.Journal of cheminformatics · 2024Article
- Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.International journal of molecular sciences · 2023Article
- GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds.Scientific reports · 2021Article
- Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit.Biomolecules · 2020Article
- Validation strategies for target prediction methods.Briefings in bioinformatics · 2020Review
- Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.PloS one · 2019Article
- A novel methodology on distributed representations of proteins using their interacting ligands.Bioinformatics (Oxford, England) · 2018Article
- YaTCM: Yet another Traditional Chinese Medicine Database for Drug Discovery.Computational and structural biotechnology journal · 2018Article
- VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.BMC bioinformatics · 2017Article
- Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict. The benchmark and a simple target prediction method to use as a performance baseline are available at http://ballester.marseille.inserm.fr/TF-benchmark.tar.gz.
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