Molecular informatics

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A graphical example of protein-ligand docking using informatics tools

Molecular informatics is an integrative field of science that examines "chemical and biological data on both the molecular and systemic level" using a wide variety of information technologies.[1]

Molecular informatics is somewhat related to pharmacoinformatics in so much as it's used often in the field of drug design and discovery for "lead compound identification, drug target identification, and hit-to-lead optimization."[1][2][3] Other applications include protein-ligand and protein-protein docking as well as biomolecular design.

History

The field of molecular informatics arguably grew out of molecular modeling[4][5][6], a tool of chemoinformatics that uses theoretical methods and computational techniques to replicate the behavior of molecules, often as a three-dimensional representation. Gradually, the fields of biology, chemistry, and informatics began to integrate, as the editors of the journal Molecular Informatics noted in a 2012 editorial:

"Later on, when the few protein structures available could be analysed with the first graphical molecular modelling packages, the automated docking of ligands into the binding cavities of proteins offered a means to generate hypotheses of protein-ligand interactions at the atomic level. These were exciting times for some of the chemists and biologists that envisaged the wealth of opportunities that integrating informatics into those traditional disciplines could offer for gaining a deeper understanding, but also widening the scope, of how small molecules interact with macromolecules."[6]

As informatics and molecular modeling began playing a more important role in chemical and biological research, the fields of bioinformatics and chemoinformatics emerged, with the concept of molecular informatics in turn forming around them.[6][2][7]

Application

Molecular informatics can help tackle problems and tasks such as the following[1][6][8][9]:

  • documenting and studying protein-ligand and protein-protein docking
  • designing, studying, and identifying pharmaceutical solutions for health problems
  • identifying "genetic and epigenetic signals related to diseases"
  • calculating chemical properties of molecular and sub-molecular interactions
  • mining small molecule and drug target databases
  • using machine learning techniques to generate models of candidate molecule properties
  • grouping molecules into practical divisions by biological and physicochemical properties

Informatics

With the advent of "Big Data," molecular informatics is turning to machine-learning tools for predictive modeling, improved 3-D visualization tools for analyzing molecular structure and function, and system-wide approaches to experimental procedures.<ref name="BaumannBD" / >


Further reading


External links

References

  1. 1.0 1.1 1.2 Baumann, Knut; Becker, Gerhard F.; Mestres, Jordi; Schneider, Gisbert (January 2011). "Molecular Informatics - The First Year". Molecular Informatics 30 (1): 3. doi:10.1002/minf.201190001. http://onlinelibrary.wiley.com/doi/10.1002/minf.201190001/full. Retrieved 03 June 2014. 
  2. 2.0 2.1 Flower, Darren R. (2002). "Molecular Informatics: Sharpening Drug Design's Cutting Edge". Drug Design: Cutting Edge Approaches. Royal Society of Chemistry. pp. 1–52. ISBN 9780854048168. http://books.google.com/books?id=Dct237W7pH4C&pg=PA41. Retrieved 03 June 2014. 
  3. Bender, Andreas; Glen, Robert C. (October 2004). "Molecular similarity: a key technique in molecular informatics". Organic & Biomolecular Chemistry 2 (22): 3204–3218. doi:10.1039/B409813G. http://pubs.rsc.org/en/content/articlelanding/2004/ob/b409813g. Retrieved 03 June 2014. 
  4. Glen, Robert (December 2002). "Developing tools and standards in molecular informatics - Interview by Susan Aldridge". Chemical Communications (23): 2745–2747. doi:10.1039/B207793K. http://pubs.rsc.org/en/content/articlelanding/2002/cc/b207793k. Retrieved 03 June 2014. 
  5. Korkin, Dmitry (2003). A New Model for Molecular Representation and Classification: Formal Approach Based on the ETS Framework. University of New Brunswick Fredericton. pp. 652. ISBN 0612988686. http://dl.acm.org/citation.cfm?id=1087511. Retrieved 03 June 2014. 
  6. 6.0 6.1 6.2 6.3 Baumann, Knut; Ecker, Gerhard F.; Mestres, Jordi; Schneider, Gisbert (January 2012). "Molecular Informatics - A Leading Discipline in a Complex Emerging Field". Molecular Informatics 31 (1): 3. doi:10.1002/minf.201290001. http://onlinelibrary.wiley.com/doi/10.1002/minf.201290001/full. Retrieved 03 June 2014. 
  7. Eriksson, L.; Johansson, E.; Kettaneh-Wold, N.; Trygg, J.; Wikström, C.; Wold, S. (2006). "Chapter 21: Chem- and Bioinformatics". Multi- and Megavariate Data Analysis, Part 2, Advanced Applications and Method Extensions. MKS Umetrics AB. pp. 85–97. ISBN 9789197373036. http://books.google.com/books?id=2CHrDa-kBSYC&pg=PA85. 
  8. Bender, Andreas; Glen, Robert C. (November 2004). "Molecular similarity: a key technique in molecular informatics". pp. 3204–3218. doi:10.1039/B409813G. PMID 15534697. http://pubs.rsc.org/en/Content/ArticleLanding/2004/OB/b409813g#!divAbstract. 
  9. Baumann, Knut; Becker, Gerhard F.; Mestres, Jordi; Schneider, Gisbert (January 2015). "Systems Approaches and Big Data in Molecular Informatics". pp. 2. doi:10.1002/minf.201580131. http://onlinelibrary.wiley.com/doi/10.1002/minf.201580131/full.