Discovery Studio Explained

Discovery Studio is a suite of software for simulating small molecule and macromolecule systems. It is developed and distributed by Dassault Systemes BIOVIA^[1] (formerly Accelrys).

The product suite has a strong academic collaboration programme,^[2] supporting scientific research and makes use of a number of software algorithms developed originally in the scientific community, including CHARMM,^[3] MODELLER,^[4] DELPHI,^[5] ZDOCK,^[6] DMol3^{[7] [8]} and more.

Scope

Discovery Studio provides software applications covering the following areas:

• Simulations
  • Including Molecular Mechanics, Molecular Dynamics, Quantum Mechanics
  • For molecular mechanics based simulations: Include implicit and explicit-based solvent models and membrane models
  • Also includes the ability to perform hybrid QM/MM calculations
• Ligand Design
  • Including tools for enumerating molecular libraries and library optimization
• Pharmacophore modeling
  • Including creation, validation and virtual screening^{[9] [10]}
• Structure-based Design
  • Including tools for fragment-based placement and refinement,^[11] receptor-ligand docking and pose refinement, de novo design
• Macromolecule design and validation
• Macromolecule engineering
  • Specialist tools for protein-protein docking^[12]
  • Specialist tools for Antibody design^[13] and optimization
  • Specialist tools for membrane-bound proteins, including GPCRs
• QSAR
  • Covering methods such as multiple linear regression, partial least squares, recursive partitioning, Genetic Function approximation and 3D field-based QSAR
• ADME
• Predictive toxicity

See also

• Molecular Mechanics Programs
• Quantum Mechanics Software
• Molecular Modeling
• Molecular Design Software
• Protein homology modeling
• MDL Chime

External links

• Accelrys.com
• Discovery Studio
• Supporting free software tools: Discovery Studio Visualizer and ActiveX Controls

Recent News Articles

• BioIT World News article on Discovery Studio
• BioInform (GenomeWeb)

Notes and References

1. https://www.3dsbiovia.com/
2. http://accelrys.com/innovation/academic-collaborator-program/
3. Brooks B. R., Brooks III C. L., Mackerell A. D., Nilsson L., Petrella R. J., Roux B., Won Y., Archontis G., Bartels C., Boresch S., Caflisch A., Caves L., Cui Q., Dinner A. R., Feig M., Fischer S., Gao J., Hodoscek M., Im W., Kuczera K., Lazaridis T., Ma J., Ovchinnikov V., Paci E., Pastor R. W., Post C. B., Pu J. Z., Schaefer M., Tidor B., Venable R. M., Woodcock H. L., Wu X., Yang W., York D. M. and Karplus M. CHARMM: The Biomolecular simulation Program, J. Comput. Chem. 2009, 30, 1545-1615.
4. Eswar N., Marti-Renom M.A., Webb B., Madhusudhan M.S., Eramian D., Shen M., Pieper U., Sali A. Comparative Protein Structure Modeling With MODELLER. Current Protocols in Bioinformatics, John Wiley & Sons, Inc., 2006, Supplement 15, 5.6.1-5.6.30.
5. W.Rocchia, E.Alexov, and B.Honig. Extending the Applicability of the Nonlinear Poisson-Boltzmann Equation: Multiple Dielectric Constants and Multivalent Ions. J. Phys. Chem. B, 2001, 105, 6507-6514.
6. Chen R., Weng Z. ZDOCK: An Initial-stage Protein-Docking Algorithm. Proteins 2003, 52, 80-87.
7. Matsuzawa N., Seto J., DixonD. A., J. Phys. Chem. A, 1997, 101, 9391.
8. Delley Bi, J. Chem. Phys., 1990, 92, 508; ibid, 1991, 94, 7245; ibid, 2000, 7756.
9. Sutter A., Jiabo L., Maynard A.J., Goupil A., Luu T., Katalin N., New Features that Improve the Pharmacophore Tools from Accelrys
10. Luu T., Malcolm N., Nadassy K., Pharmacophore Modeling Methods in Focused Library Selection -Applications in the Context of a New Classification Scheme, Comb. Chem. & High Thr. Screening, 2011, 14(6), pp. 488-499(12)
11. Haider M.K., Bertrand H.-O., Hubbard R.E., Predicting Fragment Binding Poses Using a Combined MCSS MM-GBSA Approach, J. Chem. Inf. Model., 2011, 51 (5), pp 1092–1105
12. Corradia V., Mancinib M, Santuccib M.A., Carlomagnoc T., Sanfelicec D., Moria M., Vignarolia G., Falchia F., Manettia F., Radia M., Botta M., Computational techniques are valuable tools for the discovery of protein–protein interaction inhibitors: The 14-3-3σ case
13. Almagro J.C., Beavers M.P., Hernandez-Guzman F., Maier J., Shaulsky J.,Butenhof K., Labute P., Thorsteinson N., Kelly K., Teplyakov A., Luo J., Sweet R., Gilliland G.L., Antibody modeling assessment, Proteins: Structure, Function, and Bioinformatics, 2011, 79(11), pages 3050–3066.