Evidence map›Paper›PMID 40418485›Full record

ArticleMolecular diversity2025

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Mohammadreza Torabi, Soroush Sardari, Alejandro Rodríguez-Martínez, Nooshin Arabi, Horacio Pérez-Sánchez, Fahimeh Ghasemi

Abstract read
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In one paragraph

Article in Molecular diversity, 2025. 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
–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

3 citing papers in PubMed.

  1. Review
  2. Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026
    Review
  3. Review
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.

Mohammadreza Torabi *Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Soroush Sardari *Medical Biotechnology Department, Drug Design and Bioinformatics Unit, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
Alejandro Rodríguez-MartínezComputer Engineering Department, Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain.
Nooshin ArabiDepartment of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Horacio Pérez-SánchezComputer Engineering Department, Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain. hperez@ucam.edu.
Fahimeh GhasemiDepartment of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran. f_ghasemi@amt.mui.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.

Indexed as

Angiogenesis InhibitorsDeep LearningDrug RepositioningReceptors, Fibroblast Growth FactorReceptors, Vascular Endothelial Growth FactorAngiogenesisDrug ApprovalErbB ReceptorsHumansUnited StatesUnited States Food and Drug AdministrationAngiogenesis InhibitorsErbB ReceptorsReceptors, Fibroblast Growth FactorReceptors, Vascular Endothelial Growth FactorAngiogenesis inhibitorsDeep learning networkDrug discoveryDrug repurposingRandom forest

Identifiers

What Socratic holds

Textmetadata
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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 Socratic graph.