Accelerating AutoDock Vina with GPUs

Shidi, Tang; Chen, Ruiqi; Lin, Mengru; Lin, Qingde; Zhu, Yanxiang; Ding, Ji; Hu, Haifeng; Ling, Ming; Wu, Jiansheng

doi:10.3390/molecules27093041

Cited by 67 publications

(62 citation statements)

References 38 publications

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“…In the future, we plan to develop methods for receptor optimization and docking parameters optimization before the docking screen, improving the docking and scoring by incorporating the AI-based methods, integrating the similarity scores in LBS with a more convenient ranking system, and optimizing the algorithms and pipelines to accelerate the computation by the guidance of existing similar co-crystal structures and the introduction of GPU-based Vina docking [ 66 ]. Overall, DrugRep will be more accurate, clever, easy-to-use and rapid for drug repurposing with our continuous efforts.…”

Section: Discussionmentioning

confidence: 99%

DrugRep: an automatic virtual screening server for drug repurposing

Gan

Liu

et al. 2022

Acta Pharmacol Sin

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Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

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Section: Discussionmentioning

confidence: 99%

DrugRep: an automatic virtual screening server for drug repurposing

Gan

Liu

et al. 2022

Acta Pharmacol Sin

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“…. , 𝜓 𝑁 𝑟𝑜𝑡 𝑖 } (10) where 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 correspond to the conformation position in a pre-determined searching space; 𝑎 𝑖 , 𝑏 𝑖 , 𝑐 𝑖 , 𝑑 𝑖 denote its orientation as a rigid body in the quaternion form; 𝜓 1 𝑖 , 𝜓…”

Section: Device Partmentioning

confidence: 99%

“…As all we know, GPU is an ideal means of acceleration for common users due to its low barrier, high cost-effectiveness, and ease of development. Our recently proposed Vina-GPU [10] method realized the acceleration of AutoDock Vina with GPUs, which solved the difficulty of implementing its parallel acceleration on GPUs caused by the seriality design of the AutoDock Vina algorithm. Our Vina-GPU method realized the 14-fold acceleration in one typical virtual screening case, and it achieved an average of 21-fold and a maximum of 50-fold docking acceleration in a benchmark dataset with 140 complexes against the original AutoDock Vina while ensuring their comparable docking accuracy.…”

Section: Introductionmentioning

confidence: 99%

Vina-GPU 2.0：further accelerating AutoDock Vina and its derivatives with GPUs

Ding

Shidi

Wang

et al. 2022

Preprint

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Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with GPUs is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy of their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets RIPK1 and RIPK3 from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold,1.4-fold and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2 and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface (GUI) tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

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“…Docking programs for GPUs have appeared only in the past few years. Some of them try to port traditional docking programs to GPUs, such as AutoDock-GPU (115), Vina-GPU (116), Uni-Dock (117), and MedusaDock GPU (118), while others are new docking programs tailored for GPUs (e.g. Accelerated CDOCKER (119)).…”

Section: Hardware Acceleration Of Molecular Dockings With Gpusmentioning

confidence: 99%

“…A GPU version of AutoDock Vina is therefore of great interest. One attempt was the software Viking, however, no speedup has been reported (116). The first successful GPU version of AutoDock Vina with a significant speedup, Vina-GPU, was published in 2022 (116).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Recent Developments in Structure-Based Virtual Screening Approaches

Gorgulla¹

2022

Preprint

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Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of smallmolecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions.Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform earlystage drug discovery.

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Accelerating AutoDock Vina with GPUs

Cited by 67 publications

References 38 publications

DrugRep: an automatic virtual screening server for drug repurposing

DrugRep: an automatic virtual screening server for drug repurposing

Vina-GPU 2.0：further accelerating AutoDock Vina and its derivatives with GPUs

Recent Developments in Structure-Based Virtual Screening Approaches

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