Measuring docking code performance
Here is something I've been meaning to do for a long time: measure the performance of a docking code on a standard benchmarking dataset. The choice was DUDE, the Dud38 subset. From the Dud38, I've decided to look at actives and decoys both the "scaffolds" and "final". Here, only partial results for the "scaffolds" are shown.
The code tested is autodock_vina, version 1.1.2 with the default settings (including exhaustiveness). This is the only publicly available docking code that I know of. rdkit was used to prepare the molecules from smiles strings.
The performance is measured over 38 protein targets (hence the Dud38). Experimentally known active compounds as well a fake, decoy molecules are docked and scored to each target. The scoring is then converted into a ranking of molecules. The performance metric is AUC, or area under the curve, commonly used in signal processing. The AUC of 1.0 indicates a perfect classifier (all actives score higher than the decoys), while the AUC of 0.5 indicates a random classifier.
Here is the main result
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The left-hand plot is the AUC per target, shown as a boxplot with 10 bootstrap samples used to estimate the error on the AUC. The right-hand side is the aggregate information on the 38 targets: the mean AUC is 0.6 with a standard deviation of around 0.1.
The code tested is autodock_vina, version 1.1.2 with the default settings (including exhaustiveness). This is the only publicly available docking code that I know of. rdkit was used to prepare the molecules from smiles strings.
The performance is measured over 38 protein targets (hence the Dud38). Experimentally known active compounds as well a fake, decoy molecules are docked and scored to each target. The scoring is then converted into a ranking of molecules. The performance metric is AUC, or area under the curve, commonly used in signal processing. The AUC of 1.0 indicates a perfect classifier (all actives score higher than the decoys), while the AUC of 0.5 indicates a random classifier.
Here is the main result
The left-hand plot is the AUC per target, shown as a boxplot with 10 bootstrap samples used to estimate the error on the AUC. The right-hand side is the aggregate information on the 38 targets: the mean AUC is 0.6 with a standard deviation of around 0.1.