ArticleBMC bioinformatics2009
A statistical framework to evaluate virtual screening.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- Identification and Evaluation of Natural Compounds as Potential Inhibitors of NS2B-NS3 Zika Virus Protease: A Computational Approach.Molecular biotechnology · 2025Article
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 CaJournal of chemical theory and computation · 2025Article
- A versatile information retrieval framework for evaluating profile strength and similarity.Nature communications · 2025Article
- Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors.Journal of chemical information and modeling · 2025Article
- A versatile information retrieval framework for evaluating profile strength and similarity.bioRxiv : the preprint server for biology · 2025Article
- Protein profiling uncovers IGF-1R inhibition potential of 3-(2-furoyl)-indole scaffolds in hepatocellular carcinoma.Future medicinal chemistry · 2025Article
- Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).Pharmaceuticals (Basel, Switzerland) · 2024Article
- Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.ACS central science · 2024Article
- On the relevance of query definition in the performance of 3D ligand-based virtual screening.Journal of computer-aided molecular design · 2024Article
- Synergistic effect of potential alpha-amylase inhibitors from Egyptian propolis with acarbose using in silico and in vitro combination analysis.BMC complementary medicine and therapies · 2024Article
- De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference.International journal of molecular sciences · 2022Article
- Identification of Potential Insect Growth Inhibitor againstInternational journal of molecular sciences · 2022Article
- Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Improving virtual screening results with MM/GBSA and MM/PBSA rescoring.Journal of computer-aided molecular design · 2021Article
- Validation strategies for target prediction methods.Briefings in bioinformatics · 2020Review
- Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors.Molecules (Basel, Switzerland) · 2020Article
- Article
- Structure-Based Virtual Screening: From Classical to Artificial Intelligence.Frontiers in chemistry · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundReceiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize "early recognition" have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.
resultsWe have proposed a statistical framework to evaluate VS studies by which the threshold to determine whether a ranking method is better than random ranking can be derived by bootstrap simulations and 2 ranking methods can be compared by permutation test. We found that different metrics emphasize "early recognition" differently. BEDROC and RIE are 2 statistically equivalent metrics. Our newly proposed metric SLR is superior to pROC. Through extensive simulations, we observed a "seesaw effect" - overemphasizing early recognition reduces the statistical power of a metric to detect true early recognitions.
conclusionThe statistical framework developed and tested by us is applicable to any other metric as well, even if their exact distribution is unknown. Under this framework, a threshold can be easily selected according to a pre-specified type I error rate and statistical comparisons between 2 ranking methods becomes possible. The theoretical null distribution of SLR metric is available so that the threshold of SLR can be exactly determined without resorting to bootstrap simulations, which makes it easy to use in practical virtual screening studies.
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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.