Abstract

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Highlights

  • Predicting with chemical accuracy the biological activity that a small drug-like compound can attain against its target is a major challenge in drug discovery

  • When one keeps in mind the diversity of the chemical universe that can a priori be explored [13,14,15], the ability to discriminate between actives and inactives still represents a formidable challenge that makes it necessary to resort to simplified computational approaches

  • The vast amount of drug-like chemical libraries can be explored using in silico virtual screening (VS) techniques, which encompass a variety of computational algorithms and formalisms in the search of novel bioactive molecules

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Summary

Introduction

Predicting with chemical accuracy the biological activity that a small drug-like compound can attain against its target is a major challenge in drug discovery. The combined integration of SBVS and LBVS techniques may be a promising strategy when data about both the structure of ligand-target complexes and similarity relationships to active compounds are available, leading to a holistic framework suitable to enhance the success of drug discovery projects [35,36]. Among the final hits selected for in vitro biological evaluation, compounds SD-01 and SD-02 inhibited the HDAC8 enzyme with IC50 (i.e., the concentration of inhibitor that gives half-maximal response) values of 9.0 and 2.7 nM, respectively These two examples suffice to demonstrate that a judicious choice of LB and SB techniques, adapted to the available information about the ligands and target, may be powerful in disclosing drug-like compounds.

LB and SB Strategies in VS
Schematic adopted forfor combining
Sequential LB and SB Methods
Parallel LB and SB Approaches
SBmethods methods highlighted in
Interaction-Based Methods
Methods
Similarity-Docking Strategies
Predicting the Pose of Ligands
Overview
Similarity-Guided Score Scheme
Representation
Exploiting Chemical Libraries and Biological Data
Schematic
Conclusions
Findings
Full Text
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