ReviewJournal of medicinal chemistry2013
Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis.
Review in Journal of medicinal chemistry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 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
137 citing papers in PubMed.
- Efficient Discovery of Potent AKR1B10 Inhibitors Using Co-folding Model Boltz-2.Journal of chemical information and modeling · 2026Article
- Article
- Novel Phosphodiesterase‑2 Inhibitors Discovered via Structure-Based Virtual Screening Targeting the Allosteric GAF‑B Domain.ACS medicinal chemistry letters · 2026Article
- A novel framework for the discovery of MAPK-activated protein kinase 2 (MAPKAPK2) inhibitors using a multi-feature deep learning ensemble.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- A functional genomics-pharmacotranscriptomics framework identifies host-directed anti-influenza agents.iScience · 2026Article
- Cloud-based ligand-guided virtual screening with deep-learning-enhanced docking identifies a micromolar CXCR4 antagonist.Molecular diversity · 2026Article
- In Silico Isomerization Produces Apt Negative Data for VHTS Validation.Journal of chemical information and modeling · 2026Article
- Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating ICBMC bioinformatics · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- Evaluation of Human-Approved Active Pharmaceutical Ingredients Against Lactate Dehydrogenase from Theileria annulata.Acta parasitologica · 2026Article
- Discovery of a novel CaMKⅡα inhibitor by machine learning, molecular docking and molecular dynamics simulation.Journal of computer-aided molecular design · 2026Article
- Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.Nature protocols · 2026Review
- Target-driven machine learning-enabled virtual screening (TAME-VS) enables prioritization of AKR1C3 inhibitors.Communications chemistry · 2026Article
- Food-Derived Antidiabetic Peptides as Multi-Target Systemic Regulators: A Comprehensive Review of Sources, Preparation, Mechanisms and Future Perspectives.Foods (Basel, Switzerland) · 2026Review
- Human organoids: Fit for drug discovery?Stem cell reports · 2026Review
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- The Last Mile Problem: A Critical Assessment of Physics-Based and AI Tools for Small Molecule Binding Prediction in Virtual Screening.Journal of chemical information and modeling · 2026Article
- Inhibiting NHEJ: In-Silico Approach to Stratifying DNA-PK Inhibitors, Predicting Radio-Halogenation Potential of DNA-PK Inhibitors, and Assessing AlphaFold2 Prediction Accuracy.Bioengineering (Basel, Switzerland) · 2026Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses, and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, druglike, and ADMET filters were applied to the reported hits to assess the quality of compounds reported, and a careful analysis of a subset of the studies that presented hit optimization was performed. These data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, definition of hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria.
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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.