Exact(48)
The other important issue in VS efficiency is the numerical and visual illustration of how well the VS method works.
The performance of SVM is substantially improved against several other VS method based on the same datasets and molecular descriptors, suggesting that the VS performance of SVM is primarily due to SVM classification models rather than the molecular descriptors used.
In this paper, we consider a variable-separation (VS) method to solve the stochastic saddle point (SSP) problems.
This work presents a rapid and simple synthesis procedure for ZnO nanowires (NWs) array by using the vapor solid (VS) method.
The VS method attempts to get a low rank separated representation of the solution for SSP in a systematic enrichment manner.
The VS method is applied to obtain the solution in tensor product structure for stochastic partial differential equations (SPDEs) in a mixed formulation.
Similar(12)
Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets.
Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact.
Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS methods and the model.
However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood.
Then for different VS methods we obtain different predicted ranking lists based on different prediction models.
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