Evidence map›Paper›PMID 41394085›Full record

ArticleBioinformatics advances2025

Unveiling novel drug-target couples: an empowered automated pipeline for enhanced virtual screening using AutoDock Vina.

Sveva Bonomi, Stefano Carsi, Emily Samuela Turilli-Ghisolfi, Elisa Oltra, Tiziana Alberio, Mauro Fasano

Abstract read
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Sveva BonomiDepartment of Science and High Technology, University of Insubria, Busto Arsizio (VA) 21052, Italy.ORCID https://orcid.org/0009-0001-0400-8400
Stefano CarsiDepartment of Science and High Technology, University of Insubria, Como 22100, Italy.
Emily Samuela Turilli-GhisolfiDepartment of Science and High Technology, University of Insubria, Busto Arsizio (VA) 21052, Italy.
Elisa OltraDepartment of Pathology, School of Medicine and Health Sciences, Universidad Católica de Valencia, San Vicente Mártir 46001, Spain.
Tiziana AlberioDepartment of Science and High Technology, University of Insubria, Busto Arsizio (VA) 21052, Italy.
Mauro FasanoDepartment of Science and High Technology, University of Insubria, Busto Arsizio (VA) 21052, Italy.ORCID https://orcid.org/0000-0003-0628-5871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Drug repurposing offers a cost-effective and time-efficient strategy for identifying new therapeutic uses for existing medications, capitalizing on their known safety profiles and pharmacokinetics. We present an automated virtual screening pipeline using AutoDock Vina, a molecular docking software that predicts how small molecules bind to protein targets. This pipeline enhances the speed and accuracy of drug candidate identification by automating and parallelizing the docking process. Results: We developed and validated a fully automated virtual screening pipeline based on AutoDock Vina, enabling computational parallelization and random ligand positioning without relying on prior knowledge of biologically active protein domains. As a proof of concept, the pipeline was applied to the "serotonin and anxiety" pathway. Docking results were compared with known drug-target interactions, demonstrating the ability of the pipeline to reliably identify compounds interacting with serotonin receptors. This case study confirms the pipeline's effectiveness in supporting drug repurposing by identifying promising candidates for further experimental validation. Availability and implementation: The AutoDock Vina automation pipeline is freely available for noncommercial use at https://gitlab.com/la_sveva/pip2.0. It is compatible with Linux systems, and a Docker image is provided for ease of deployment and reproducibility. Researchers can easily integrate the pipeline into existing workflows, supporting broader adoption in virtual screening and drug repurposing projects.

Identifiers

PMID41394085
PMCPMC12699991

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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.