Journal of Molecular Modeling

, Volume 15, Issue 2, pp 193–196 | Cite as

Accessible haptic technology for drug design applications

  • Nicola Zonta
  • Ian J. Grimstead
  • Nick J. Avis
  • Andrea BrancaleEmail author
Original Paper


Structure-based drug design is a creative process that displays several features that make it closer to human reasoning than to machine automation. However, very often the user intervention is limited to the preparation of the input and analysis of the output of a computer simulation. In some cases, allowing human intervention directly in the process could improve the quality of the results by applying the researcher intuition directly into the simulation. Haptic technology has been previously explored as a useful method to interact with a chemical system. However, the need of expensive hardware and the lack of accessible software have limited the use of this technology to date. Here we are reporting the implementation of a haptic-based molecular mechanics environment aimed for interactive drug design and ligand optimization, using an easily accessible software/hardware combination.


De novo Drug design Haptic Lead optimization ZODIAC 


  1. 1.
    Irwin JJ, Shoichet BK (2005) ZINC – a free Database of Commercially Available Compounds for Virtual Screening. J Chem Inf Model 45:177–182. doi: 10.1021/ci049714+ CrossRefGoogle Scholar
  2. 2.
    Kitchen DB, Decornez H, Furr JR et al (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949. doi: 10.1038/nrd1549 CrossRefGoogle Scholar
  3. 3.
    Kontoyianni M, Madhav P, Suchanek E et al (2008) Theoretical and practical considerations in virtual screening: a beaten field? Curr Med Chem 2:107–116. doi: 10.2174/092986708783330566 CrossRefGoogle Scholar
  4. 4.
    Evans EA Rapid prototyping and industrial design practice: can haptic feedback modeling provide the missing tactile link? Rapid Prototyping J 11:153–159. doi: 10.1108/13552540510601273
  5. 5.
    Vidal FP, Chalmers N, Gould DA et al (2005) Developing a needle guidance virtual environment with patient specific data and force feedback. Proceeding of the 19th International Congress of CARS - International Congress Series 1281:418–423Google Scholar
  6. 6.
    Laycock SD, Day AM (2007) A survey of haptic rendering techniques. Comput Graph Forum 26:50–65. doi: 10.1111/j.1467-8659.2007.00945.x CrossRefGoogle Scholar
  7. 7.
    Ouh-Young M, Pique M, Hughes J et al (1988) Using a manipulator for force display in molecular docking. Proc IEEE Robot Autom Conf 3:1824–1829Google Scholar
  8. 8.
    Meyer EF, Swanson SM, Williams JA (2000) Molecular modeling and drug design. Pharmacol Ther 85:113–121. doi: 10.1016/S0163-7258(99)00069-8 CrossRefGoogle Scholar
  9. 9.
    Nagata H, Mizushima H, Tanaka H (2002) Concept and prototype of protein-ligand docking simulator with force feedback technology. Bioinformatics 18:140–146. doi: 10.1093/bioinformatics/18.1.140 CrossRefGoogle Scholar
  10. 10.
    Daunay B, Micaelli A, Regnier S (2007) 6 DOF haptic feedback for molecular docking using wave variables. IEEE International Conference on Robotics and Automation 840–845.Google Scholar
  11. 11.
    Persson PB, Cooper MD Tibell L et al (2007) Designing and evaluating a haptic system for biomolecular Education. IEEE Virtual Reality Conference 171–178Google Scholar
  12. 12.
    Wollacott AM, Mertz KM (2007) Haptic applications for molecular structure manipulation. J Mol Graph Model 25:801–805. doi: 10.1016/j.jmgm.2006.07.005 CrossRefGoogle Scholar
  13. 13.
    Halgren T (1995) Merk Molecular Force Field I Basis Form ScopeParameterization and Performance of MMFF94. J Comp Chem 17:490–519CrossRefGoogle Scholar
  14. 14.
    Halgren T (1995) Merk Molecular Force Field II MMFF94 van der Waals and Electrostatic Parameters for Intermolecular Interactions. J Comp Chem 17:520–552CrossRefGoogle Scholar
  15. 15.
    Halgren T (1995) Merk Molecular Force Field III Molecular Geometries and Vibrational Frequencies for MMFF94. J Comp Chem 17:553–586CrossRefGoogle Scholar
  16. 16.
    Halgren T (1995) Merk Molecular Force Field IV Conformational Energies and Geometries for MMFF94. J Comp Chem 17:587–615Google Scholar
  17. 17.
    Halgren T (1995) Merk Molecular Force Field V Extension of MMFF94. Using Experimental Data Additional Computational Data and Empirical Rules. J Comput Chem 17:616–641. doi: 10.1002/(SICI)1096-987X(199604)17:5/6<616::AID-JCC5>3.0.CO;2-X CrossRefGoogle Scholar
  18. 18.
    Calculations were performed using a Viglen Genie with a dual core Intel Xeon E5335 processor with 2Gb of RAM running Windows XP professionalGoogle Scholar
  19. 19.
    ZODIAC 05b –
  20. 20.
    Rajarshi G, Michael TH, Geoffrey R et al (2006) The Blue Obelisk – Interoperability in Chemical Informatics. J Chem Inf Model 46:991–998. doi: 10.1021/ci050400b CrossRefGoogle Scholar
  21. 21.
    Novint FALCON –
  22. 22.
    SensAble Technologies, Inc. –
  23. 23.
    De Martino G, La Regina G, Coluccia A et al (2004) Arylthioindoles Potent Inhibitors of Tubulin Polymerization. J Med Chem 47:6120–6123. doi: 10.1021/jm049360d CrossRefGoogle Scholar
  24. 24.
    De Martino G, Edler MC, La Regina G et al (2006) New Arythioindoles Potent Inhibitors of Tubulin Polymerization 2 Structure Activity Relationship and Molecular Modeling Studies. J Med Chem 49:947–954. doi: 10.1021/jm050809s CrossRefGoogle Scholar
  25. 25.
    Romagnoli R, Baraldi PG, Carrion MD et al (2007) Synthesis and Biological Evaluation of 2-and 3-Amino Benzo[b]Thiophene Derivatives as Antimitotic Agents and Inhibitors of Tubulin Polymerization. J Med Chem 50:2865–2874. doi: 10.1021/jm061479u CrossRefGoogle Scholar
  26. 26.
    Brancale A, Silvestri R (2007) Indole a core nucleus for potent inhibitors of tubulin polymerization. Med Res Rev 27:209–238. doi: 10.1002/med.20080 CrossRefGoogle Scholar
  27. 27.
    Wang R, Gao Y, Lai L (2000) LigBuilder: A Multi-Purpose Program for Structure-based Drug Design. J Mol Model 6:498–516. doi: 10.1007/s0089400060498 CrossRefGoogle Scholar
  28. 28.
    Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649–663. doi: 10.1038/nrd1799 CrossRefGoogle Scholar
  29. 29.
    Honma T (2003) Recent advances in de novo design strategy for practical lead identification. Med Res Rev 23:606–632. doi: 10.1002/med.10046 CrossRefGoogle Scholar
  30. 30.
    Demonstration videos of ZODIAC can be found here:

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Nicola Zonta
    • 1
  • Ian J. Grimstead
    • 2
  • Nick J. Avis
    • 2
  • Andrea Brancale
    • 1
    Email author
  1. 1.Welsh School of PharmacyCardiff UniversityWalesUK
  2. 2.School of Computer ScienceCardiff UniversityWalesUK

Personalised recommendations