Evidence map›Paper›PMID 37513905›Full record

ReviewPharmaceuticals (Basel, Switzerland)2023

Computer-Aided Identification of Kinase-Targeted Small Molecules for Cancer: A Review on AKT Protein.

Erika Primavera, Deborah Palazzotti, Maria Letizia Barreca, Andrea Astolfi

Open access · goldAbstract readReview
In one paragraph

Review in Pharmaceuticals (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 24% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Anti-cancer agents in medicinal chemistry · 2025
    Article
  3. 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

4 authors at 1 institution in 1 country.

Erika PrimaveraDepartment of Pharmaceutical Sciences, "Department of Excellence 2018-2022", University of Perugia, 06123 Perugia, Italy.ORCID 0009-0000-7065-3128
Deborah PalazzottiDepartment of Pharmaceutical Sciences, "Department of Excellence 2018-2022", University of Perugia, 06123 Perugia, Italy.
Maria Letizia BarrecaDepartment of Pharmaceutical Sciences, "Department of Excellence 2018-2022", University of Perugia, 06123 Perugia, Italy.
Andrea AstolfiDepartment of Pharmaceutical Sciences, "Department of Excellence 2018-2022", University of Perugia, 06123 Perugia, Italy.ORCID 0000-0003-3337-2372
University of Perugia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AKT (also known as PKB) is a serine/threonine kinase that plays a pivotal regulatory role in the PI3K/AKT/mTOR signaling pathway. Dysregulation of AKT activity, especially its hyperactivation, is closely associated with the development of various human cancers and resistance to chemotherapy. Over the years, a wide array of AKT inhibitors has been discovered through experimental and computational approaches. In this regard, herein we present a comprehensive overview of AKT inhibitors identified using computer-assisted drug design methodologies (including docking-based and pharmacophore-based virtual screening, machine learning, and quantitative structure-activity relationships) and successfully validated small molecules endowed with anticancer activity. Thus, this review provides valuable insights to support scientists focused on AKT inhibition for cancer treatment and suggests untapped directions for future computer-aided drug discovery efforts.

Indexed as

AKTcancercomputer-aided drug discoverydockingkinase inhibitorsmachine learningpharmacophoreQSARvirtual screening

Identifiers

PMID37513905
PMCPMC10384952
OpenAlexW4384038380

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.