ArticleMolecules (Basel, Switzerland)2022
Accelerating AutoDock Vina with GPUs.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Article
- AI-enhanced adaptive virtual screening of large libraries for ligand discovery.Nature biotechnology · 2026Article
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- AI-Assisted Identification of a Putative Allosteric Ligand Targeting the CDK4/Cyclin D1 Protein-Protein Interface.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A programmable bifunctional flavoenzyme for direct amine-to-ester conversion.Science advances · 2026Article
- Structural Basis of Anthocyanin-Mediated Modulation of IL-2, IL-17, and TNF-α: A Docking and Molecular Dynamics Study.International journal of molecular sciences · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Predicting peroxisome proliferator-activated receptor gamma potency of small molecules: a synergistic consensus model and deep learning binding affinity approach powered by Enalos Cloud Platform.Molecular diversity · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
- Virtual screening and molecular dynamics simulations for drug repurposing against autophagy to attenuate blast in cereal plants.Scientific reports · 2026Article
- In silico discovery of thioglycoside analogues as donor-site inhibitors of glycosyltransferase LgtC.Scientific reports · 2026Article
- GPU-Accelerated Virtual Screening and Molecular Dynamics Simulations for Identification of Novel DPP‑4 Inhibitors.ACS omega · 2026Article
- Integrative analysis of transcriptome and single-cell sequencing combined with experimental validation identifies biomarkers associated with T cell and senescence in sepsis.Scientific reports · 2026Article
- Gut microbial metabolites targeting JUN in renal cell carcinoma via IL-17 signaling pathway: network pharmacology approach.Molecular diversity · 2026Article
- Exploring the Potential Molecular Targets of Cyanidin-3-O-glucoside for Type 2 Diabetes Mellitus Treatment.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Identification of potent high-affinity secondary nucleation inhibitors of Aβ42 aggregation from an ultra-large chemical library using deep docking.Molecular systems biology · 2026Article
- Computational insights into a protease inhibitor from Streptomyces globosus VITSMAB-2 molecular docking and dynamics simulations against SARS-CoV-2 main protease.Scientific reports · 2025Article
- Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.ChemistryOpen · 2025Article
- Exploring the mechanism of plasticizers action in liver damage based on network toxicology, molecular docking, and molecular dynamic simulation.Scientific reports · 2025Article
- Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson's disease.Hereditas · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.
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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.