(Isostar) and geometric (Mogul) knowledgebases (vide infra). These tools are used in
a number of ways such as in structure checking, conformation generation and
refinement restraints. The significance of these tools has reached critical mass, and
they have become mainstream applications embedded into a range of different
research ecosystems. For example, the Hydrogen Bond Propensity tool and Full
Interaction Maps are used by the pharmaceutical industry in their workflows for the
evaluation of polymorphism risk.
2.3 The Effect on Crystallographic Practice
The recent advances in experimental equipment and approaches outlined above
clearly have an impact on the body of structural data that the community can accrue.
This phenomenon clearly has the potential to be completely transformative; however, it is not without associated difficulties, and there are several factors that the
community need to address and change practice in order to account for the following
aspects.
2.3.1 Rate of Data Generation
The speed at which diffractometers can collect data has ramped up, generally in a
stepped manner coinciding with the introduction of revolutionary technology. The
last decade has seen the largest step up of all, with data collection being at least an
order of magnitude faster now than it was at the beginning. The facility that is built
around these instruments must therefore adapt, and this is a very difficult process –
coping with change is generally a problem, especially if that means adjusting or
rebuilding infrastructure. So how does the modern small molecule crystallography
facility cope with this increased rate of data collection and volume of results
generated?
The macromolecular crystallography community has very successfully addressed
this through increasing levels of automation, and nowadays, the approaches in this
field are very different to those of 10 years ago. Looking to this field and making
comparisons is therefore a valuable exercise – and much of the following section will
be presented in this way. Some attempts at developing facility operations akin to MX
approaches have been made, e.g. [47, 72]. However, the culture and purpose of
much of the field of small molecule crystallography is very different and is not
necessarily so suited to automation approaches. In the life sciences, crystallography
is one of a set of tools that a structural biologist will use to address a very specific
problem, whereas small molecule crystallographers look at a much wider-ranging set
of problems and often operate as a service to those making new compounds and
materials.
There are two significant impacts on the facility when the rate of data collection
increases so markedly. Firstly, in-house data management approaches need to adapt
98
S. J. Coles et al.
a number of ways such as in structure checking, conformation generation and
refinement restraints. The significance of these tools has reached critical mass, and
they have become mainstream applications embedded into a range of different
research ecosystems. For example, the Hydrogen Bond Propensity tool and Full
Interaction Maps are used by the pharmaceutical industry in their workflows for the
evaluation of polymorphism risk.
2.3 The Effect on Crystallographic Practice
The recent advances in experimental equipment and approaches outlined above
clearly have an impact on the body of structural data that the community can accrue.
This phenomenon clearly has the potential to be completely transformative; however, it is not without associated difficulties, and there are several factors that the
community need to address and change practice in order to account for the following
aspects.
2.3.1 Rate of Data Generation
The speed at which diffractometers can collect data has ramped up, generally in a
stepped manner coinciding with the introduction of revolutionary technology. The
last decade has seen the largest step up of all, with data collection being at least an
order of magnitude faster now than it was at the beginning. The facility that is built
around these instruments must therefore adapt, and this is a very difficult process –
coping with change is generally a problem, especially if that means adjusting or
rebuilding infrastructure. So how does the modern small molecule crystallography
facility cope with this increased rate of data collection and volume of results
generated?
The macromolecular crystallography community has very successfully addressed
this through increasing levels of automation, and nowadays, the approaches in this
field are very different to those of 10 years ago. Looking to this field and making
comparisons is therefore a valuable exercise – and much of the following section will
be presented in this way. Some attempts at developing facility operations akin to MX
approaches have been made, e.g. [47, 72]. However, the culture and purpose of
much of the field of small molecule crystallography is very different and is not
necessarily so suited to automation approaches. In the life sciences, crystallography
is one of a set of tools that a structural biologist will use to address a very specific
problem, whereas small molecule crystallographers look at a much wider-ranging set
of problems and often operate as a service to those making new compounds and
materials.
There are two significant impacts on the facility when the rate of data collection
increases so markedly. Firstly, in-house data management approaches need to adapt
98
S. J. Coles et al.