Newest Ultra Model Sets 40 - 42 [HOT]
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As explained, the size of the test_set library is limited (for reasons of robustness, computational cost and fairness), and although ChemPLP2.0 is typically selected for this purpose, performance will vary depending on the specific library that is used. It is important to note that the training and test sets may contain many molecules that are identical to each other, and thus they may not be truly unbiased, because the same molecule may be in the training and test sets. In such a case, the molecules in the test set are often a subset of the molecules in the training set (because of the molecular feature extraction step).
A typical DD cycle using the ChemPLP2.0 library as the training set and the 'test_set' library as the target set took approximately 2 minutes on an Intel Core i7-6700HQ @ 2.6GHz CPU (or slower) and 8GB RAM running DD 7.0 and the latest version of the scikit-docking Python package.
We used a 0.7 Pearson's correlation similarity threshold to filter molecule pairs. To maintain the same number of molecules in the training and test sets, we removed ~67% of the molecules in the test_set.
In this section, we've presented the DD protocol and described how it was used to repurpose ChemPLP2.0 as a predictive docking tool in a machine learning approach. We've demonstrated that DD allows for fast, accurate docking of large libraries (1B+ molecules) and can be used for in silico screening in a similar way to the previously published DOCK. Although the results produced by DD were not reported for this study, in all cases the method produced an accuracy comparable to that of DOCK when tested using the unbiased and high-throughput benchmark set ChemPLP2.0. We believe that DD can readily be used for unbiased virtual screening, and potentially more refined screening with the aid of expert-derived datasets.
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The total number of correctly predicted molecules is displayed in the row for the model you just best (in best_model_stats.txt). The graph (on the right) displays the number of molecules that the model predicts correctly for each of the 10 features on which the model best performs, along with the percentage of molecules that were correctly predicted.
The difference between the predicted and best known binding affinity is displayed in the row for the model. The graph (on the right) shows the difference for each of the 10 features (on the x-axis) for which the model best performs. The error is calculated as the difference between the predicted and best known binding affinities for each molecule and the model. 827ec27edc