Abstract
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
Highlights
The drug discovery process required to enable a new compound to reach the market as an innovative therapeutic entity is significantly expensive and time-consuming (Mullard, 2014; DiMasi et al, 2016; Mignani et al, 2016)
The development of an empirical scoring function requires three components (Pason and Sotriffer, 2016): (i) descriptors that describe the binding event, (ii) a dataset composed of three-dimensional structure of diverse protein–ligand complexes associated with the corresponding experimental affinity data, and (iii) a regression or classification algorithm to calibrate the model establishing a relationship between the descriptors and the experimental affinity
The development of accurate empirical scoring functions to predict protein–ligand binding affinities is a key aspect in Structure-based drug design (SBDD)
Summary
The drug discovery process required to enable a new compound to reach the market as an innovative therapeutic entity is significantly expensive and time-consuming (Mullard, 2014; DiMasi et al, 2016; Mignani et al, 2016). The fast evaluation of docking poses generated by the search method and the accurate prediction of binding affinity of topranked poses is essential in VS protocols In this context, scoring functions emerge as a straightforward and fast strategy despite limited accuracy, remaining as the main alternative to be applied in VS experiments (Huang et al, 2010). The development of an empirical scoring function requires three components (Pason and Sotriffer, 2016): (i) descriptors that describe the binding event, (ii) a dataset composed of three-dimensional structure of diverse protein–ligand complexes associated with the corresponding experimental affinity data, and (iii) a regression or classification algorithm to calibrate the model establishing a relationship between the descriptors and the experimental affinity. We will adopt the nomenclature “linear” for the MLR scoring functions and “nonlinear” for models trained with more complex machinelearning techniques
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