Evidence map›Paper›PMID 25072167›Full record

ReviewMethods (San Diego, Calif.)2015

Hierarchical virtual screening approaches in small molecule drug discovery.

Ashutosh Kumar, Kam Y J Zhang

Abstract readReview
In one paragraph

Review in Methods (San Diego, Calif.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 1 pooled it
–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

59 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  8. DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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  9. Sulfide regulation and catabolism in health and disease.Signal transduction and targeted therapy · 2025
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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

2 authors.

Ashutosh KumarStructural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan.
Kam Y J ZhangStructural Bioinformatics Team, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan. Electronic address: kamzhang@riken.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Indexed as

Molecular Docking SimulationDrug DiscoveryDrug Evaluation, PreclinicalSmall Molecule LibrariesSmall Molecule LibrariesHierarchical virtual screeningMolecular dockingPharmacophore modelingSimilarity search

Identifiers

PMID25072167
PMCPMC7129923

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.