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The approach we used in this study relies on the use of logistic regression to model how the scores issued by virtual screening methods explain the activity of the compounds in a virtual screening experiment.
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Interestingly, a similar compound, the organic dye Disperse Blue 56 (2-chloro-1,5-diamino-4,8-dihydroxyanthraquinone) was identified by virtual screening method as an inhibitor of PaPMM/PGM with an IC50 of 5 μM [ 42].
By monitoring the performances of three virtual screening methods using the predictiveness curve, TG and pTG on the DUD dataset, we have proposed a new approach to define optimal score thresholds adjusted to each target.
The version of MDDR used here was that originally described by Hert et al. and used subsequently, by both us and others, for the validation of virtual screening methods [34 36].
These results illustrate difficulty of evaluating virtual screening methods and importance of evaluating methods by using several different metrics.
These datasets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods.
Displaying the PC then allows for an intuitive analysis of the performances of virtual screening methods.
Good predictions issued from virtual screening methods yield steeper predictiveness curves corresponding to wider variations of activity probabilities.
Generally speaking, current virtual screening methods mainly focus on single drug-target interaction.
This questions the practicality of the virtual screening methods that use local minimum conformations as potential bound conformations.
These results both support the virtual screening methods that use local minimum conformations and caution about possible adverse effect of excessive energy minimization on generating databases of ligand conformations for virtual screening.
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