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We empower our customers and give them the tools to fully immerse themselves in the virtual world. Our in-headset tutorials get them accustomed to the controllers and game-to-game navigation in the SpringboardVR environment.

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Atomic Versus Virtual Subsystems

Subsystems can be virtual or atomic. Simulink ignores virtual subsystem boundaries when determining block update order. By contrast, Simulink executes all blocks within an atomic subsystem before moving on to the next block. Conditionally executed subsystems are atomic. Unconditionally executed subsystems are virtual by default. You can, however, designate an unconditionally executed subsystem as atomic (see Subsystem). This is useful if you need to ensure that a subsystem is executed in its entirety before any other block is executed.

In the music library section, DJs can organise their tracks into crates in Serato DJ Pro, or virtual folders in Virtual DJ. Smart crates/folders automatically fill a crate with any track in the library that meets the criteria you set.

Understanding the deformation behavior of metallic materials at high strain rates requires the characterization of plasticity contributors, such as twins, phase transformed regions, and dislocations. However, predicting the contributions from phase transformation and twinning relies on a complete understanding of the selection of variants for various loading orientations and the evolution of their volume fractions. This manuscript presents a new virtual texture (VirTex) analysis approach to characterize phase transformation and twinning variants in deformed microstructures generated using molecular dynamics (MD) simulations. The VirTex method involves the construction of a rotation matrix to calculate the angle/axis pairs and misorientation angles for each atom in the microstructure. Any changes in the orientation angle from angle/axis pairs and/or structure types are analyzed to determine the nucleation and evolution of variants in the microstructure. The study uses shock deformed single-crystal Fe, Ta, and Cu to analyze the variant selections for phase transformation or twinning or both in BCC and FCC systems. In addition, the VirTex analysis is able to characterize the phase transformation and twinning variants in nanocrystalline Fe and Ta microstructures. Besides characterizing variants, orientation mapping also provides an accelerated and on-the-fly approach for quantifying twin fractions in MD microstructures.

Nonvirtual subsystems play an active role in the simulation ofa system. If you add or remove a nonvirtual subsystem, you change the model behavior.Nonvirtual subsystems are atomic subsystems that execute as asingle block, or atomic unit, when the parent model executes.

Ligand-based virtual screening approaches all rely on the concept that structurally similar molecules have similar biological activities [22]. Molecular fingerprints are bitwise representations of molecular structure and properties and examples include hashed connectivity pathways [23], dictionary-based [24], and layered atom fingerprints [25, 26]. Another example are the 3D-MoRSE descriptors [27]. These methods are also called two-dimensional (2D) similarity methods since these do not rely on the underlying three-dimensional (3D) structure of the molecules.

It has been shown that in a significant number of cases ligand-based virtual screening outperforms protein structure-based virtual screening [28], although the latter performs better in scenarios where novel scaffolds need to be identified [29]. 3D similarity virtual screening methods make use of the three-dimensional structure of the reference compound, as a query to search for compounds that have similar spatial atomic arrangements. These methods are not dependent upon the underlying molecular topology of the query compounds and are therefore also useful for scaffold hopping. Examples of such algorithms include shape-matching algorithms and shape-based fingerprints [30, 31], molecular field descriptors [32, 33], pharmacophore fingerprints [34,35,36] and pharmacophore-based screening [37,38,39]. A number of recent reviews on the use of descriptors and classification methods are available [40,41,42,43].

In order to demonstrate the applicability of spectrophores in the domain of virtual screening, mathematical models were generated from the spectrophores, and these models were subsequently used to identify compounds predicted to be inhibitory active against a particular subset of therapeutic targets. Following this virtual screening step, a number of these compounds were actually acquired and their predicted inhibitory activity was subsequently biochemically validated.

The spectrophore is a novel descriptor that reflects, in a virtual manner, the way how molecules are binding to a set of artificial receptors, taking into account the spatial interactions between a molecule and its surroundings. Because of these unique properties, the spectrophore can be considered to be a one-dimensional mathematical description of a three-dimensional pharmacophore. This makes it applicable for a wide range of cheminformatics approaches, including virtual screening using sophisticated statistical models and clustering approaches. Successful applications in the area of scaffold hopping and virtual screening have been demonstrated in this study.

In the multi-target virtual screening experiment, all compounds were treated as neutral and they were not ionized according to their physiological pH. This could be one of the factors explaining the poor model quality of the thrombin target (Table 6), as it has been demonstrated that many of the thrombin inhibitors carry a positively charged functional group as a common feature binding into the P1 pocket of thrombin [60]. Although the current setup was sufficient for our novel proof-of-concept study such as this work, in a real-world virtual screening experiment the correct pretreatment and washing conditions for each compound would need to be carefully determined [61].

GT, WL and HDW are the original inventors of the spectrophore approach. FMDS coded and wrote the precision and recall code on the DUD-E dataset (application note 1). FMDS also coded the C++/Python implementation of the spectrophore code. KA reviewed the manuscript. RG designed and performed the biological experiments. HDW coded the scaffold hopping and virtual screening application (application notes 2 and 3), supervised the project and wrote the majority of the paper. All authors read and approved the final manuscript.

Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estimating the binding orientation of a query protein-ligand pair and a corresponding binding affinity score. Over the recent years, classical and modern machine learning architectures have shown potential for outperforming traditional docking algorithms. Nevertheless, most learning-based algorithms still rely on the availability of the protein-ligand complex binding pose, typically estimated via docking simulations, which leads to a severe slowdown of the overall virtual screening process. A family of algorithms processing target information at the amino acid sequence level avoid this requirement, however, at the cost of processing protein data at a higher representation level. We introduce deep neural virtual screening (DENVIS), an end-to-end pipeline for virtual screening using graph neural networks (GNNs). By performing experiments on two benchmark databases, we show that our method performs competitively to several docking-based, machine learning-based, and hybrid docking/machine learning-based algorithms. By avoiding the intermediate docking step, DENVIS exhibits several orders of magnitude faster screening times (i.e., higher throughput) than both docking-based and hybrid models. When compared to an amino acid sequence-based machine learning model with comparable screening times, DENVIS achieves dramatically better performance. Some key elements of our approach include protein pocket modeling using a combination of atomic and surface features, the use of model ensembles, and data augmentation via artificial negative sampling during model training. In summary, DENVIS achieves competitive to state-of-the-art virtual screening performance, while offering the potential to scale to billions of molecules using minimal computational resources.

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