ArticleJournal of cheminformatics2017
The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability.
Article in Journal of cheminformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery.International journal of molecular sciences · 2026Article
- Virtual Screening-Guided Identification of Candidate USP7-Inhibitory Scaffolds Exhibiting Antiproliferative Activity.Pharmaceuticals (Basel, Switzerland) · 2026Article
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- Blink reflex as a prognostic indicator for patients with prolonged disorders of consciousness: a prospective case-control study.BMJ open · 2025Article
- Article
- Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).Pharmaceuticals (Basel, Switzerland) · 2024Article
- Ligand-Based Compound Activity Prediction via Few-Shot Learning.Journal of chemical information and modeling · 2024Article
- Article
- Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines.RSC medicinal chemistry · 2020Article
- Structure-Based Virtual Screening: From Classical to Artificial Intelligence.Frontiers in chemistry · 2020Review
- Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.Molecules (Basel, Switzerland) · 2019Article
- Chemoinformatics and structural bioinformatics in OCaml.Journal of cheminformatics · 2019Article
- Combined In Vitro Studies and in Silico Target Fishing for the Evaluation of the Biological Activities ofMolecules (Basel, Switzerland) · 2018Article
- Comment on "The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability".Journal of cheminformatics · 2018Article
- Reply to the comment made by Šicho, Vorśilák and Svozil on 'The Power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability'.Journal of cheminformatics · 2018Article
- Classifiers and their Metrics Quantified.Molecular informatics · 2018Article
- Consensus queries in ligand-based virtual screening experiments.Journal of cheminformatics · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new metric for the evaluation of model performance in the field of virtual screening and quantitative structure-activity relationship applications is described. This metric has been termed the power metric and is defined as the fraction of the true positive rate divided by the sum of the true positive and false positive rates, for a given cutoff threshold. The performance of this metric is compared with alternative metrics such as the enrichment factor, the relative enrichment factor, the receiver operating curve enrichment factor, the correct classification rate, Matthews correlation coefficient and Cohen's kappa coefficient. The performance of this new metric is found to be quite robust with respect to variations in the applied cutoff threshold and ratio of the number of active compounds to the total number of compounds, and at the same time being sensitive to variations in model quality. It possesses the correct characteristics for its application in early-recognition virtual screening problems.
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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.