Evidence map›Paper›PMID 22922346›Full record

ReviewActa pharmacologica Sinica2012

Computational drug discovery.

Si-Sheng Ou-Yang, Jun-Yan Lu, Xiang-Qian Kong, Zhong-Jie Liang, Cheng Luo, Hualiang Jiang

Abstract readReview
In one paragraph

Review in Acta pharmacologica Sinica, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing 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

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

  1. Pooled it
  2. Review
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  13. MolSnapper: Conditioning Diffusion for Structure-Based Drug Design.Journal of chemical information and modeling · 2025
    Article
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50 more citing papers are in PubMed but not listed here.

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

6 authors.

Si-Sheng Ou-YangDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, China.
Jun-Yan Lu
Xiang-Qian Kong
Zhong-Jie Liang
Cheng Luo
Hualiang Jiang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

Indexed as

Computer-Aided DesignDrug DesignAnimalsComputational BiologyDrug DiscoveryHumansModels, MolecularMolecular Docking Simulation

Identifiers

PMID22922346
PMCPMC4003107

What OpenQuestion holds

Textmetadata
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.