In silico Drug Design

Abstract

In the field of computational drug design, the identification and characterisation of the biological target of interest is a major step. Despite the growing number of such resolved protein structures every year, there are still many drug targets, especially membrane proteins, for which structural determination is very difficult. In these cases, experimental knowledge on already determined bioactive molecules may be used successfully for computational ligand‐based drug‐design methods. However, for the past three decades, most drug discovery efforts have been driven by the structure of the target biomolecule. Advances in structural biology methods have provided structural information of many molecules, giving rise to the structure‐based drug‐design process as a powerful tool for drug discovery in research academia and pharmaceutical industry. Both fields in the in silico drug‐design field are commonly used, each one depending on background experimental information and relevant computational methodologies.

Key Concepts

  • The main computer‐aided drug‐design approaches are either ligand or structure based.
  • Main ligand‐based methods for identifying bioactive compounds are chemical similarity, pharmacophore mapping and QSAR.
  • Advances in structural biology methodologies have greatly assisted structure‐based drug design.
  • Structure‐based methods in computational drug design are mostly molecular docking, molecular dynamics, fragment‐based drug‐design and pharmacophore modelling.
  • Docking and pharmacophore methodologies are most commonly used for virtual screening in drug design.

Keywords: in silico; drug design; computational; virtual screening; drug discovery

Figure 1. Main methodologies for in silico ligand‐ and structure‐based drug design.
Figure 2. Pharmacophore mapping for antihypertensive agents (sartans) on a ligand‐based pharmacophore generation. Pharmacophore features are depicted in spheres; cyan for hydrophobic, blue for negative charge and green for hydrogen bond acceptor (arrow indicates the direction of the hypothetical hydrogen bond). Molecules are shown in orange (losartan), blue (valsartan), yellow (olmesartan) and white (irbesartan). Adapted with permission from Matsoukas et al., 2013. Copyright (2013) American Chemical Society.
Figure 3. Computational docking of a Candida albicans inhibitor, MK51 (green), to the binding site of the haem‐bearing (cyan) protein (white). Adapted from Smiljkovic et al., 2018 with permission from John Wiley and Sons. Copyright 2018.
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Further Reading

Merz KM, Ringe D and Reynolds C (2010) Drug Design: Structure‐ and Ligand‐Based Approaches. Cambridge, UK: Cambridge University Press.

Sliwoski G, Kothiwale S, Meiler J and Lowe ED Jr (2014) Computational methods in drug discovery. Pharmacological Reviews 66: 334–395.

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Young DC (2009) Computational Drug Design: A Guide for Computational and Medicinal Chemists. Hoboken, New Jersey: John Wiley & Sons, Inc..

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Makrynitsa, Garyfallia I, Lykouras, Michail, Spyroulias, Georgios A, and Matsoukas, Minos‐Timotheos(Aug 2018) In silico Drug Design. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0028112]