ArticleJournal of chemical information and modeling2013
Comparing neural-network scoring functions and the state of the art: applications to common library screening.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Structure-Based Virtual Screening and In Silico Evaluation of Marine Algae Metabolites as Potential α-Glucosidase Inhibitors for Antidiabetic Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.Molecular systems biology · 2022Article
- Article
- Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.PLoS computational biology · 2018Article
- Scoria: a Python module for manipulating 3D molecular data.Journal of cheminformatics · 2017Article
- The Performance of Several Docking Programs at Reproducing Protein-Macrolide-Like Crystal Structures.Molecules (Basel, Switzerland) · 2017Article
- Homology modeling and virtual screening studies of FGF-7 protein-a structure-based approach to design new molecules against tumor angiogenesis.Journal of chemical biology · 2016Article
- Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.Journal of chemical information and modeling · 2015Article
- Machine-learning techniques applied to antibacterial drug discovery.Chemical biology & drug design · 2015Article
- Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.Wiley interdisciplinary reviews. Computational molecular scienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.
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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.