70% of the 1,600 responses favoured a combination approach that would utilise both computational and analytical methods to tackle structure determination and characterisation
Structure determination and characterisation underpins many chemical and biological applications from the fundamental pursuits of understanding protein folding to the cutting-edge synthesis of novel drugs.
While the variety of methods used to model molecular entities is diverse, they can be divided into two broad classes of approaches: computational methods and analytical methods.
The Science Advisory Board queried its members as to which of these two categories would be most effective given the financial constraints of today's research budgets.
70% of the 1,600 responses favoured a combination approach that would utilise both computational and analytical methods to tackle structure determination and characterisation.
Computational methods are based on molecular simulation or quantum mechanics calculations.
Molecular simulation, in particular, is well suited for examining the structures of large molecules like proteins.
In contrast, quantum mechanics allows for highly precise calculations on smaller molecular systems or on those systems with more complex chemistry.
Analytical methods allow for a molecule's structure to be derived through such quantitative techniques as nuclear magnetic resonance, mass spectrometry and high performance liquid chromatography.
Because the results from many analytical techniques can be predicted computationally, simulation can be used to interpret and explain analytical results.
This process can also be inverted so that a model of a particular structure can be developed from the analytical data. "I believe that this synergy allows for an effective utilization of both methods to determine and characterize molecular structure," claims Tamara Zemlo, director of scientific and medical communications of the Science Advisory Board, "and this combination approach will be instrumental in ensuring more cost-effective drug development in the pharmaceutical industry."
