Key Topics in Molecular Docking for Drug Design

Abstract

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Keywords: benchmarking sets; computer-aided drug design; consensus methods; fragment-based; machine learning; structure-based drug design.

Figure 1

General workflow of molecular docking calculations. The approaches normally start by obtaining 3D structures of target and ligands. Then, protonation states and partial charges are assigned. If not previously known, the target binding site is detected, or a blind docking simulation may be performed. Molecular docking calculations are carried out in two main steps: posing and scoring, thus generating a ranked list of possible complexes between target and ligands.

Figure 2

Scopus search results for the query “TITLE-ABS-KEY (software AND docking) AND PUBYEAR > 1994 AND PUBYEAR < 2019” where the word software is substituted for one of the eight most common docking software or by the word consensus.

Figure 3

Ratio of the numbers of papers containing either the expression “molecular docking” or “ligand docking” to the number of papers containing either of the two expressions AND the word consensus.

Figure 4

Learning methods can be broadly divided into supervised learning, when there is data available for training and parameterisation; and unsupervised learning, when there is no such data. Unsupervised learning cannot be used for binding affinity predictions and virtual screening. Supervised learning, on the other hand, can be divided into parametric and nonparametric learning. Parametric learning assumes a predetermined functional form, as observed in linear regression, and is the method employed in classical scoring functions. Nonparametric learning, or just machine learning, does not presume a predetermined functional form, which is instead inferred from the data itself. It can yield continuous output, as in nonlinear regression, or discrete output, for classification problems such as binders/nonbinders identification.