ArticleMolecules (Basel, Switzerland)2025
High-Throughput, High-Quality: Benchmarking GNINA and AutoDock Vina for Precision Virtual Screening Workflow.
Article in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery.International journal of molecular sciences · 2026Article
- Article
- Cloud-based ligand-guided virtual screening with deep-learning-enhanced docking identifies a micromolar CXCR4 antagonist.Molecular diversity · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- ImagiChem: Hybrid Deterministic Image-Conditioned Generation of Chemically Valid and Drug-like Molecules from Artistic Inputs.ACS omega · 2026Article
- SMASH: Screening Molecules Accurately on Small Hardware. Fast, user-friendly, enhanced with a machine learning virtual screening tool.Journal of molecular modeling · 2026Article
- In Silico Drug Repositioning Identifies SYK Kinase Inhibitors as Potential Neuroprotective Agents for Ataxia-Telangiectasia.Chemical biology & drug design · 2026Article
- RiboScreenBiomedicines · 2026Review
- A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.Journal of chemical information and modeling · 2026Article
- Absolute Binding Free Energy Calculations between the SARS-CoV-2 Main Protease and 130 Drug Leads Using Implicit Ligand Theory.Journal of chemical information and modeling · 2026Article
- Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.Journal of computer-aided molecular design · 2026Review
- Integrated Multi-Omics Analysis Explores the Protective Effects and Potential Mechanisms ofBiomolecules · 2026Article
- Structure-Guided Repurposing of Approved Drugs Identifies Aprepitant and Mavorixafor as Putative δ-Opioid Receptor Agonist Candidates.International journal of molecular sciences · 2026Article
- Review
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Article
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Authors and funding
2 authors.
Funding
Abstract
Drug discovery is an intricate and resource-intensive process in which computational approaches, such as molecular docking, are essential, particularly in the early stages, to identify potential hits. However, docking still has many drawbacks, including problems in managing protein flexibility and the reliability of scoring functions. In this paper, we systematically compared the performance of AutoDock Vina, one of the most widely used open-source docking tools, with GNINA. This advanced evolution integrates convolutional neural networks (CNNs) for pose scoring. The comparison was conducted on ten heterogeneous protein targets, including metalloenzymes, kinases, and G-protein-coupled receptors (GPCRs). With the ability to accurately replicate binding poses and their energy values, GNINA showed outstanding performance in both virtual screening (VS) of active ligands and re-docking steps of co-crystallized ligands. GNINA's enhanced ability to accurately distinguish between true positives and false positives-a specificity not found with AutoDock Vina-is confirmed by ROC curves and Enrichment Factor (EF) results. Therefore, we propose an integrated GNINA-based workflow that can significantly enhance the quality and reliability of docking results, providing a valuable tool for optimizing the initial stages of drug discovery.
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Registered trials
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.