ArticleJournal of chemical information and modeling2019
Practical Model Selection for Prospective Virtual Screening.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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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.
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Who cites it
28 citing papers in PubMed.
- Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.Journal of cheminformatics · 2026Article
- Two faces of drug repurposing data in machine learning-driven structure-based virtual screening supported by interaction fingerprints.Journal of computer-aided molecular design · 2026Article
- Computation-driven discovery of novel chromone derivatives for the treatment of triple-negative breast cancerActa pharmaceutica Sinica. B · 2026Article
- Integrating traditional and modern approaches for comprehensive pharmacophore map validation in drug discovery.Journal of computer-aided molecular design · 2026Review
- Article
- Efficient decoy selection to improve virtual screening using machine learning models.Journal of cheminformatics · 2025Article
- Design and screening of novel molecular compounds targeting lactate dehydrogenase of Babesia microti.Parasites & vectors · 2025Article
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.Journal of cheminformatics · 2024Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.Journal of cheminformatics · 2023Article
- Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries.Journal of chemical information and modeling · 2023Article
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- Molecular docking in organic, inorganic, and hybrid systems: a tutorial review.Monatshefte fur chemie · 2023Review
- Bayes optimal informer sets for early-stage drug discovery.Biometrics · 2023Article
- Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons.Journal of chemical information and modeling · 2023Article
- Target-Specific Machine Learning Scoring Function Improved Structure-Based Virtual Screening Performance for SARS-CoV-2 Drugs Development.International journal of molecular sciences · 2022Article
- Computer-Aided Drug Design Boosts RAS Inhibitor Discovery.Molecules (Basel, Switzerland) · 2022Review
- Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with deep graph networks.PLoS computational biology · 2022Article
- Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.International journal of molecular sciences · 2022Review
- Screening S protein - ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19.European journal of medicinal chemistry · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well on public data sets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.
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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.