# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 1 Introduction  
The use of computational methods is a crucial part of the drug discovery, development, and optimization process. Protein-ligand docking and virtual screening are two of the most used techniques in this field that continue to show promise in hit identification and subsequent optimization [1]. They are also 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. They do, however, possess few limitations [5; 6]. 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]. Docking, however, still faces difficulties, particularly regarding the correct modeling of ligand and protein flexibility [15; 16; 17; 18] and of water-mediated interactions [18; 19]. It is widely used for small molecules, but its use for small peptides and other larger biomolecules has only been under development in the last decade [20; 21; 22]. Typically, the docking software is an interplay between the search algorithm, which explores and generates different poses of the ligand, and the scoring function, which estimates the binding affinitiesof the poses previously created, discriminating between the best and not so good alternatives [1; 5]. This estimate, which in some cases is a prediction of the free energy of binding, must be able to discriminate between molecules that bind to the target and those that do not [23]. When looking at the two enantiomers, for example, it is still not possible to identify the most active form with most of the scoring functions used by the most common docking software [24]. Even with all the significant improvements in computational power and docking software, considering all interactions that happen when a ligand binds to its target is an extremely challenging task. In order to be rigorous, the scoring functions would have to be much more complex, involve quantum calculations and, thus, these assays would turn out to be considerably expensive and time-consuming. When applying a virtual screening protocol, one wishes to screen very large databases of compounds in a relatively small period of time and, therefore, scoring functions are often simplified to improve the speed and cost of the computational screenings [6]. These simplifications come with a cost in accuracy, which might not be problematic for one ligand-target situation but takes a much more challenging scope when talking about virtual screening of thousands or millions of compounds [5]. The goal of virtual screening (VS) is to guide the selection of molecules for experimental testing. In these assays, millions of compounds are docked into one specific target and only a selection of the top scores proceeds for experimental testing. If a scoring functions fails to identify a potential strong binder, then, it remains hidden among those million compounds, despite their pharmacological potential. In fact, that is one of the main problems in VS, the false negatives, or molecules that the docking fails to identify as strong ligands. False positives are also a problem, that is, molecules that are incorrectly identified as strong binders. These molecules, however, are easily discarded in the preliminary experimental assays [25]. It is, however, difficult to compare the performance of different docking alternatives because each software handles the target and ligand in different manners. Additionally, it has been shown that the docking and VS results vary according to the type of target and ligand molecule [26; 27; 28; 29; 30].