Abstract
AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.
Highlights
The use of computational methods is a crucial part of the drug discovery, development, and optimization process
Some programs and scoring functions are better able to capture some of these characteristics, while other show improved performance in targets with other features
The average results obtained for the set of 101 target showed that AutoDock and Vina exhibit a similar average performance in discriminating between ligands and decoys
Summary
The use of computational methods is a crucial part of the drug discovery, development, and optimization process. They are helpful tools for drug repositioning [2,3,4] These methods are effective and fast, and allow researchers to evaluate large virtual databases of molecular compounds as a first attempt to guide the selection of more limited sets of compounds for experimental testing. Protein–ligand docking is a computational technique that predicts the conformation and orientation (pose) of a ligand when it is bound to a given protein [1,7,8,9,10,11,12] With this method, the ligand-target interactions are modeled to achieve an optimal complementarity of steric and physicochemical properties [13]. This methodology has made possible the visualization of the potential interactions between a ligand and its target [14]
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