Abstract
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set () or the test set (e.g., specificity, selectivity or ). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.
Highlights
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects
We present an improved version of enrichment optimization algorithm (EOA) and demonstrate its superior performances in the virtual screening of five protein targets, this time in comparison with the most common VS approach, namely, molecular docking
In terms of the different protein targets, the best results were obtained for urokinase-type plasminogen activator (UROK) and TRY1, followed by human immunodeficiency virus type protease (HIVPR), whereas models derived for ACES and MK14 gave overall poorer results
Summary
Time and money are two of the most required resources in the design of new drugs and materials. A appealing yet somewhat less common approach to virtual screening is presented by QSAR equations derived from easy to calculate 1D, 2D and sometimes global These models were typically not used for virtual screening due to the computational resources required for large scale docking. Irrespective of the exact nature of the evaluation metric, there is no reason to a priori assume that any of these metrics could reliably assess the performances of a QSAR model in the context of VS This is because the task faced by VS, namely, the identification of a set of weakly active compounds from within a large pool of diverse, mostly inactive compounds, is quite different from the ability to qualitatively or quantitatively predict the activities of a small set of similar compounds. We present an improved version of EOA and demonstrate its superior performances in the virtual screening of five protein targets, this time in comparison with the most common VS approach, namely, molecular docking
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