Evidence map›Paper›PMID 31683588›Full record

ArticleMarine drugs2019

Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach.

Giuseppe Floresta, Davide Gentile, Giancarlo Perrini, Vincenzo Patamia, Antonio Rescifina

Open access · goldAbstract read
In one paragraph

Article in Marine drugs, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

13 citing papers in PubMed, 26 citations in OpenAlex.

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  7. Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.International journal of molecular sciences · 2022
    Review
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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

5 authors at 1 institution in 1 country.

Giuseppe FlorestaDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. giuseppe.floresta@unict.it.ORCID 0000-0002-0668-1260
Davide GentileDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. davide.gentile@unict.it.
Giancarlo PerriniDepartment of Chemical Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. gperrini@unict.it.
Vincenzo PatamiaDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. vincenzo.patamia@unict.it.ORCID 0000-0002-0048-2631
Antonio RescifinaDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. arescifina@unict.it.ORCID 0000-0001-5039-2151
University of Catania · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have received interest following the recent publication of their pharmacologically beneficial effects. Recently, it was revealed that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered, but, unfortunately, none have reached the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches have been used for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. We retrieved 14,492 compounds from this database and filtered through them with a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 97-331 nM. ADMET property prediction was performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives. From these analyses, three molecules that are excellent candidates for becoming new drugs were found.

Indexed as

Drug DesignBiological ProductsDiabetes Mellitus, Type 2Fatty Acid-Binding ProteinsHumansHypoglycemic AgentsLigandsModels, MolecularMolecular StructureSmall Molecule LibrariesBiological ProductsFABP4 protein, humanFatty Acid-Binding ProteinsHypoglycemic AgentsLigandsSmall Molecule LibrariesA-FABPantiatherosclerosisanticancerantidiabetesantiobesityaP2computational toolscomputer-aided drug discoveryFABP4

Identifiers

PMID31683588
PMCPMC6891735
OpenAlexW2971818020

What OpenQuestion holds

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LicenceCC BY
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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.