In silico virtual screening approaches for anti-viral drug discovery

Abstract

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:

Structure versus function—The impact of computational methods on the discovery of specific GPCR–ligands

“…For an extensive review of different virtual screening methods see Murgueitio et al [26] and Lavecchia et al [27]. A second promising source of novel GPCR modulators is the de novo design of desired compounds which aims at building new chemical entities from scratch [28].…”

“…The core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, is both the main advantage and the main drawback of pharmacophore modeling [60]. 3D pharmacophores are easy understandable and simple to modify, which allows researchers to incorporate their knowledge about a specific binding mode into a transparent, but still highly descriptive model [26]. Therefore pharmacophore models can be applied for the retrospective explanation of binding modes and ligand affinities but also as a powerful tool for virtual screening.…”

“…In order to assess the quality of the obtained poses, empirical, forcefield-based, knowledge-based or consensus scoring functions can be used [46]. Whereas pose generation works reliably to sample possible ligand geometries, the performance of scoring functions heavily varies depending on the target protein, mainly due to the challenging task of predicting the entropic component of ligand binding [26]. The main attempts for docking in drug design are virtual screening, binding mode prediction and the estimation of ligand binding affinity.…”

Descriptors and their selection methods in QSAR analysis: paradigm for drug design

“…Such 2D methods rely on the calculation and comparison of molecular properties with the aim of identifying molecules that are similar with respect to the query molecule. Compared with 3D (or structure-based) methods, 2D approaches require substantially lower calculation times and, therefore, are mostly used as preliminary filters to reduce the number of compounds that can be used for further screening in later stages of drug development [4]. These 2D approaches are widely used in academia, industry, and research institutions worldwide.…”

In Silico Molecular Docking Studies and Design of Dengue Virus Inhibitors

“…Virtual in silico screening has frequently confirmed to be useful to assemble the special challenges of antiviral drug discovery. Various virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically [11].…”