In silico Drug Design


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., . 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., with permission from John Wiley and Sons. Copyright 2018.


Acharya C, Coop A, Polli JE, et al. (2011) Recent advances in ligand‐based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Current Computer‐Aided Drug Design 7 (1): 10–22.

Allen WJ, Balius TE, Mukherjee S, et al. (2015) DOCK 6: impact of new features and current docking performance. Journal of Computational Chemistry 36 (15): 1132–1156.

Bajusz D, Rácz A and Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint‐based similarity calculations? Journal of Cheminformatics 7 (1): 20.

Carlson HA and Jorgensen WL (1995) An extended linear response method for determining free energies of hydration. The Journal of Physical Chemistry 99 (26): 10667–10673.

Dhanik A and Kavraki LE (2012) Protein–ligand interactions: computational docking. In: eLS. John Wiley & Sons, Ltd.

Dixon SL, Smondyrev AM, Knoll EH, et al. (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of Computer‐Aided Molecular Design 20 (10–11): 647–671.

Doak BC, Norton RS and Scanlon MJ (2016) The ways and means of fragment‐based drug design. Pharmacology & Therapeutics 167: 28–37.

van Drie JH (2003) Pharmacophore discovery‐lessons learned. Current Pharmaceutical Design 9 (20): 1649–1664.

Gorelik B and Goldblum A (2008) High quality binding modes in docking ligands to proteins. Proteins: Structure, Function, and Bioinformatics 71 (3): 1373–1386.

Grant BJ, Lukman S, Hocker HJ, et al. (2011) Novel allosteric sites on Ras for lead generation. PLoS One 6 (10): e25711.

Guner O, Clement O and Kurogi Y (2004) Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Current Medicinal Chemistry 11 (22): 2991–3005.

Hospital A, Goñi JR, Orozco M, et al. (2015) Molecular dynamics simulations: advances and applications. Advances and Applications in Bioinformatics and Chemistry: AABC 8: 37–47.

Huang S‐Y and Zou X (2010) Advances and challenges in protein‐ligand docking. International Journal of Molecular Sciences 11 (8): 3016–3034.

Ishchenko AV and Shakhnovich EI (2002) Small molecule growth 2001 (SMoG2001): an improved knowledge‐based scoring function for protein–ligand interactions. Journal of Medicinal Chemistry 45 (13): 2770–2780.

Jones G, Willett P, Glen RC, et al. (1997) Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 267 (3): 727–748.

Kapetanovic I (2008) Computer‐aided drug discovery and development (CADDD): in silico‐chemico‐biological approach. Chemico‐Biological Interactions 171 (2): 165–176.

Katsila T, Spyroulias GA, Patrinos GP, et al. (2016) Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal 14: 177–184.

Keiser MJ, Roth BL, Armbruster BN, et al. (2007) Relating protein pharmacology by ligand chemistry. Nature Biotechnology 25 (2): 197.

Kent JT (1983) Information gain and a general measure of correlation. Biometrika 70 (1): 163–173.

Kitchen DB, Decornez H, Furr JR, et al. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews. Drug Discovery 3 (11): 935.

Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discovery Today 11 (13–14): 580–594.

Koes DR and Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. Journal of Chemical Information and Modeling 51 (6): 1307–1314.

Korb O, Stutzle T and Exner TE (2009) Empirical scoring functions for advanced protein–ligand docking with PLANTS. Journal of Chemical Information and Modeling 49 (1): 84–96.

Kumar A, Voet A and Zhang K (2012) Fragment based drug design: from experimental to computational approaches. Current Medicinal Chemistry 19 (30): 5128–5147.

Lee C‐H, Huang H‐C and Juan H‐F (2011) Reviewing ligand‐based rational drug design: the search for an ATP synthase inhibitor. International Journal of Molecular Sciences 12 (8): 5304–5318.

Lešnik S, Štular T, Brus B, et al. (2015) LiSiCA: a software for ligand‐based virtual screening and its application for the discovery of butyrylcholinesterase inhibitors. Journal of Chemical Information and Modeling 55 (8): 1521–1528.

Mao KZ (2004) Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics 34 (1): 629–634.

Marshall GR, Barry CD, Bosshard HE, et al. (1979) The Conformational Parameter in Drug Design: The Active Analog Approach. ACS Publications, Computer‐Assisted Drug Design 112: 205–226.

Matsoukas M‐T, Cordomí A, Ríos S, et al. (2013) Ligand binding determinants for angiotensin II type 1 receptor from computer simulations. Journal of Chemical Information and Modeling 53 (11): 2874–2883.

Matsoukas M‐T, Aranguren‐Ibáñez Á, Lozano T, et al. (2015) Identification of small‐molecule inhibitors of calcineurin‐NFATc signaling that mimic the PxIxIT motif of calcineurin binding partners. Science Signaling 8 (382): ra63.

McConkey BJ, Sobolev V and Edelman M (2002) The performance of current methods in ligand–protein docking. Current Science 83 (7): 845–856.

McInnes C (2007) Virtual screening strategies in drug discovery. Current Opinion in Chemical Biology 11 (5): 494–502.

Meng X‐Y, Zhang H‐X, Mezei M, et al. (2011) Molecular docking: a powerful approach for structure‐based drug discovery. Current Computer‐Aided Drug Design 7 (2): 146–157.

Morris GM, Goodsell DS, Halliday RS, et al. (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19 (14): 1639–1662.

Nair PC, Malde AK, Drinkwater N, et al. (2012) Missing fragments: detecting cooperative binding in fragment‐based drug design. ACS Medicinal Chemistry Letters 3 (4): 322–326.

O'Boyle NM, Banck M, James CA, et al. (2011) Open Babel: an open chemical toolbox. Journal of Cheminformatics 3 (1): 33.

Poptodorov K, Luu T and Hoffmann RD (2006) Pharmacophore model generation software tools. Methods and Principles in Medicinal Chemistry 32: 17.

Scott DE, Coyne AG, Hudson SA, et al. (2012) Fragment‐based approaches in drug discovery and chemical biology. Biochemistry 51 (25): 4990–5003.

Shoichet BK, Stroud RM, Santi DV, et al. (1993) Structure‐based discovery of inhibitors of thymidylate synthase. Science 259 (5100): 1445–1450.

Smiljkovic M, Matsoukas MT, Kritsi E, et al. (2018) Nitrate esters of heteroaromatic compounds as Candida albicans CYP51 enzyme inhibitors. ChemMedChem 13 (3): 251–258.

Sterling T and Irwin JJ (2015) ZINC 15–ligand discovery for everyone. Journal of Chemical Information and Modeling 55 (11): 2324–2337.

Stumpfe D and Bajorath J (2011) Similarity searching. Wiley Interdisciplinary Reviews: Computational Molecular Science 1 (2): 260–282.

Sunseri J and Koes DR (2016) Pharmit: interactive exploration of chemical space. Nucleic Acids Research 44 (W1): W442–W448.

Todeschini R and Consonni V (2009) Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/Volume II: Appendices, References, vol. 41, 2nd edition. Weinheim: John Wiley & Sons.

Totrov M and Abagyan R (2001) Protein‐ligand docking as an energy optimization problem. Drug‐receptor Thermodynamics: Introduction and Applications 1: 603–624.

Trott O and Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31 (2): 455–461.

Vlachakis D, Fakourelis P, Megalooikonomou V, et al. (2015) DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit. PeerJ 3: e725.

Vogt AD and Di Cera E (2012) Conformational selection or induced fit? A critical appraisal of the kinetic mechanism. Biochemistry 51 (30): 5894–5902.

Wolber G and Langer T (2005) LigandScout: 3‐D pharmacophores derived from protein‐bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling 45 (1): 160–169.

Wolber G, Seidel T, Bendix F, et al. (2008) Molecule‐pharmacophore superpositioning and pattern matching in computational drug design. Drug Discovery Today 13 (1–2): 23–29.

Zhang S (2011) Computer‐aided drug discovery and development. Methods in Molecular Biology 716: 23–38.

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.

Yang S‐Y (2012) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discovery Today 15: 444–450.

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

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
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. [doi: 10.1002/9780470015902.a0028112]