Evidence map›Paper›PMID 30996784›Full record

ArticleACS medicinal chemistry letters2019

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow.

Simone Sciabola, Paolo Benedetti, Giulia D'Arrigo, Rubben Torella, Massimo Baroni, Gabriele Cruciani, Francesca Spyrakis

Abstract read
In one paragraph

Article in ACS medicinal chemistry letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Simone SciabolaPfizer Worldwide Research and Development, 1 Portland Street, Cambridge, Massachusetts 02139, United States.
Paolo BenedettiDepartment of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy.
Giulia D'ArrigoDepartment of Drug Science and Technology, University of Turin, Via Pietro Giuria 9, 10125 Torino, Italy.
Rubben TorellaPfizer Worldwide Research and Development, 1 Portland Street, Cambridge, Massachusetts 02139, United States.
Massimo BaroniMolecular Discovery Ltd., Centennial Park, WD6 3FG Borehamwood, Hertfordshire, U.K.
Gabriele CrucianiDepartment of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy.
Francesca SpyrakisDepartment of Drug Science and Technology, University of Turin, Via Pietro Giuria 9, 10125 Torino, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns. Here we prospectively applied the MD-FLAP workflow to discover novel chemotypes inhibiting the Casein Kinase 1 delta (CSNK1D) enzyme. We first obtained a VS model able to separate active from inactive compounds, with a global AUC of 0.9 and a partial ROC enrichment at 0.5% of 0.18, and use it to mine the internal Pfizer screening database. Seven active molecules sharing a phenyl-indazole scaffold, not yet reported among CSNK1D inhibitors, were found. The most potent inhibitor showed an IC

Identifiers

PMID30996784
PMCPMC6466522

What OpenQuestion holds

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Registered trials

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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.