Evidence map›Paper›PMID 34766282›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

In Silico Design of Chemically Modified Cell-Penetrating Peptides.

Vinod Kumar, Gajendra P S Raghava

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. 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

2 authors at 1 institution in 1 country.

Vinod KumarDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Gajendra P S RaghavaDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. raghava@iiitd.ac.in.
Indraprastha Institute of Information Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past few decades, a large number of cell-penetrating peptides (CPPs) have been discovered. These CPPs have a wide range of applications including drug delivery vehicles. Numerous in silico tools have been developed over the years to design and predict the cell-penetrating peptides that contain natural amino acids. The majority of natural cell-penetrating peptides have several limitations including stability, immunogenicity as well as got entrapped in the cell's endosomes. The chemical modification is commonly used to most of these limitations. An in silico tool called CellPPDMod have been developed by our group to predict cell-penetration potential of chemically modified peptides. This chapter is dedicated for designing therapeutically important cell-penetrating peptides using CellPPDMod ( http://webs.iiitd.edu.in/raghava/cellppdmod/ ).

Indexed as

Computational BiologyAmino AcidsCell-Penetrating PeptidesDrug Delivery SystemsEndosomesAmino AcidsCell-Penetrating PeptidesChemical modified cell-penetrating peptidesDrug delivery vehiclesMachine learning (prediction)

Identifiers

PMID34766282
OpenAlexW3213853645

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.