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

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.