Evidence map›Paper›PMID 33376199›Full record

ReviewStroke and vascular neurology2020

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.

Huiqin He, Benquan Liu, Hongyi Luo, Tingting Zhang, Jingwei Jiang

Abstract readReview
In one paragraph

Review in Stroke and vascular neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
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

5 authors.

Huiqin HeJiangsu Key Lab of Drug Screening, China Pharmaceutical University, Nanjing, China.
Benquan LiuJiangsu Key Lab of Drug Screening, China Pharmaceutical University, Nanjing, China.
Hongyi LuoJiangsu Key Lab of Drug Screening, China Pharmaceutical University, Nanjing, China.
Tingting ZhangJiangsu Key Lab of Drug Screening, China Pharmaceutical University, Nanjing, China.
Jingwei JiangInstitute of Pharmacologic Science, China Pharmaceutical University, Nanjing, China jiangjingwei@cpu.edu.cn.ORCID 0000-0002-9163-3633

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

Indexed as

Artificial IntelligenceBig DataComputer-Aided DesignData MiningDrug DesignDrug DiscoveryAnimalsDatabases, ProteinHumansLigandsMolecular StructureMolecular Targeted TherapyPharmaceutical PreparationsProtein ConformationProteinsStructure-Activity RelationshipLigandsPharmaceutical PreparationsProteins3D structureartificial intelligencebig datahidden allosteric sitesnovel drugsundruggable targets

Identifiers

PMID33376199
PMCPMC7804061

What OpenQuestion holds

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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.