Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

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.

Keywords: binding affinity prediction; machine learning; molecular docking; scoring function; structure-based drug design; virtual screening.

FIGURE 1

The structure of α-amylase complexed with the inhibitor MeG2-GHIL (PDB code 1U33) as (A) provided by PDBbind and (B) after manual preparation. Bad and favorable polar contacts are highlighted in orange and green dashes, respectively. D, aspartate; E, glutamate or glutamic acid; H, histidine; R, arginine.