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A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented.
One of the most commonly used machine learning methods in virtual screening is Support Vector Machines (SVM) [37, 38].
Development of automatic path planning methods in virtual manufacturing tools supporting deformable parts is therefore highly motivated.
This paper describes successful engineering methods in virtual hot stamping process with tailored properties and of warm and hot formed aluminum parts.
The application of machine learning methods in virtual screening experiments is connected with the desire to correctly select potentially active compounds, also with structures that are different from the already known ligands.
Using multiple searches provides a simple way of increasing the precision of a similarity search, and thus provides a justification for the use of data fusion methods in virtual screening.
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The estimations of cumulative incidence of death made by the Kalbfleisch and Prentice method in virtual center 2 were correct, but they cannot be interpreted alone.
A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years.
This work shows that and how such structural similarity methods used in virtual screening can be improved further by integrating background knowledge on binding-relevant structural features.
In this paper, we present a concept of how structural similarity based methods used in virtual screening can be improved by integrating chemical background knowledge in the form of binding relevant or informative structural elements.
Our goal was to explore the soundness and adequacy of research methods used in virtual communities with a view to identifying problems that might be avoided in designing a project about HPV infections and HPV vaccination.
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