1,733,508 research outputs found
Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units
No full textModern 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 graphics processing units (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 in 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 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
White Oak Hub Company - Vina, Franklin County, Alabama
No full textMan using lathe on wheel hub. The White Oak Hub Company was still being operated in the 1950s by Wallace Ross Massey, Sr. Massey is standing to the far left. Massey was born in Vina on October 14, 1910. Massey died in Nashville, Tennessee in April 198
Orpowell/autodock-vina-automator:
No full textThe first(ish) official release of Autodock Vina Automato
Orpowell/autodock-vina-automator: v1.0.0
No full textThe first official release of the AutoDock Vina Automator
Vina-GPU 2.0：further accelerating AutoDock Vina and its derivatives with GPUs
No full textModern 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
Accelerating AutoDock Vina with GPUs
No full textAutoDock 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
Vina 3
No full textA long-necked plucked lute used primarily in south Indian classical music. The instrument is plucked with the right hand while the left hand depresses the strings to achieve the desired notes. The Vina is made of several parts: the body is a hollow shell made from a single piece of wood. The neck is also a hollow piece and has a resonator that is screwed into a metal cup on the back of the neck and located near the head of the instrument. The player is seated between the body and the resonator, with the instrument lying across the lap. The head is bent back and has a dragon carved into the wood. There are four pegs on the head, one of which is accessible through a hinged compartment in the head, and three pegs on the neck.
The instrument has seven strings that are attached to the bottom of the instrument with small metal rings for tuning, strung over a bridge and wound around the wooden pegs in the neck and head. The four strings that are attached to the head pegs are strung over a wooden bench-shaped bridge that has a small, thin metal plate placed on top. The three strings that are attached to the pegs on the neck run over a bent metal arch that extends from the right side of the wooden bridge (the metal arch is a separate piece that is glued to the bridge).Poor; bowl is missing from neck; several broken strings; missing metal arch
Improving Ligand-Ranking of AutoDock Vina by Changing the Empirical Parameters
No full textAutoDock Vina (Vina) achieved a very high docking-success rate, p ̂, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ̂ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R_set1=0.556±0.025 compared with R_Default=0.493±0.028 obtained by the original Vina and R_(Vina 1.2)=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (R_set1=0.621±0.016) than the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina
Ballade des Dames du temps jadis
No full textDans cette ballade Villon met en scène des dames du passé et par cela évoque admirablement la fragilité de la vie et la nostalgie de ces dames disparues, ces dames « du temps jadis » qui vivaient avant lui et qui ont été emportées par le vent. Nous y voyons sa nostalgie d’un passé lointain. En fait, il parle des femmes qu’on reconnaît à peine même à son époque, des femmes qui appartiennent à une époque reculée. François Villon évoque ces dames du temps jadis en descendant de la mythologie et de l’Antiquité jusqu’à son époque à lui. Il parle, en fait, de femmes à peine reconnaissables. Même le nom de certaines d’entre elles indique qu’elles appartiennent à un passé lointain, à une époque reculée. Il parle de femmes aux noms de Bietris, d’Alis, de Haremburgis et de la reine Blanche comme lys, dont on ne connaît même pas le nom. Mais chaque femme est l’héroïne d’une histoire dans laquelle elle joue le rôle principal
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