Molecular Docking: Shifting Paradigms in Drug Discovery

Abstract

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Keywords: adverse drug reactions; drug discovery; drug repurposing; molecular docking; polypharmacology; reverse screening; target fishing.

Figure 1

Main applications of molecular docking in current drug discovery. Molecular docking is currently employed to help rationalizing ligands activity towards a target of interest and to perform structure-based virtual screening campaigns, similarly to as when it was first developed. Besides these applications, it can also be used to identify series of targets for which the ligands present good complementarity (target fishing and profiling), some of them being potentially responsible for unexpected drug adverse reactions (off-targets prediction). Moreover, docking is also currently employed for the identification of ligands that simultaneously bind to a pool of selected targets of interest (polypharmacology) and for identifying novel uses for chemical compounds with already optimized safety profiles (drug repositioning).

Figure 2

Integration of docking with ligand-based, molecular dynamics, binding free energy approaches, artificial intelligence (AI), and statistical methods. According to the available information, different in silico approaches can be combined with docking to generate integrated workflows with improved prediction performances. Different approaches can also be combined to integrate docking (e.g., molecular dynamics and binding free energy estimations can be combined with docking to improve virtual screening results). Likewise, different approaches can also be applied at different phases of the screening workflow to improve docking predictions. For example, molecular dynamics could be combined with AI-based methods to identify suitable receptor conformations for docking. Then, ligand-based approaches could be applied for rescoring the predicted docking poses [50,65,66].