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He suggests that Robert Scaratt implanted the suggestion that Canning had been held at the Wells's house, as a useful decoy, and that he had somehow been involved in an unwanted pregnancy.
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Furthermore, we compared our approach to the optimal assignment with equal edge weights and two literature similarity functions on a subset of the Directory of Useful Decoys using sophisticated virtual screening performance metrics.
We illustrate the use of predictiveness curves as a complement to ROC on the results of a virtual screening of the Directory of Useful Decoys datasets using three different methods (Surflex-dock, ICM, Autodock Vina).
Next, we use the Directory of Useful Decoys, Enhanced (DUD-E) to evaluate the performance of Vina in virtual screening.
We used the directory of useful decoys (DUD) [23] dataset to retrospectively compare ePlatton and data fusion methods.
Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening.
As database for the approach B experiments, we use the Directory of useful Decoys that is designed to avoid bias in docking and screening studies.
For example, Liebeschuetz et al. [21] evaluated the pose prediction and the performance of Gold scoring functions using the Directory of Useful Decoys (DUD) dataset [22], which consists of 40 varied protein targets.
In a more independent test using the directory of useful decoys (DUD) set, the enrichment ratio given by these two parameters when combined with docking scores was improved to 200 300% as compared to that using scoring function alone.
The method was benchmarked on the Directory of Useful Decoys (DUD) datasets [4], using the same methodology described in the paper on the assessment of LASSO [5] and DOCK.
The dictionary of useful decoys (DUD) [16] dataset was used to perform virtual screening (VS) studies.
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