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.

Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

“…The development of deep learning scoring functions has been already attempted, but results have shown various degrees of success which could be due to a lack of appropriate datasets. [32,33] Likely, as the very nature of docking is approximate, the improvements are likely to come from better approximation of physical-chemical processes, including solvation, enthalpic and entropic factors, rather than from a better training base and procedures. [34,35] Thus, our method represents not just feasible, but also practical options for utilizing deep learning in virtual screening.…”

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

“…The term structure-based virtual screening (SBVS), often denoted as target-based VS, encompasses methods that exploit the three-dimensional (3D) structure of the target. The most widely used SBVS technique is molecular docking, which uses the structural and chemical complementarity resulting from the interaction between a fragment-like or drug-like compound and its target receptor, predicting the preferred pose of ligands in the binding site through the use of scoring functions, often supplemented with pharmacophoric constraints [ 20 , 21 , 22 , 23 ]. On the other hand, ligand-based virtual screening (LBVS) relies on the structural information and physicochemical properties of the chemical scaffold of known active and inactive molecules, which are examined under the molecular similarity principle [ 24 ].…”

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

“…Molecular docking is a computational approach used to predict the position, orientation, and the binding scores of small molecules to proteins (Torres et al, 2019). Hence, as the F2L process is commonly addressed as a combinatorial problem, molecular docking is a method that can be used in combination with other approaches to enhance the F2L process, and to increase the chances to convert a fragment hit into higher affinity ligands.…”