Automation of Crystallization by an Academic Group

Jean-Claude Bradley — Sun, 04 May 2008 08:57:54 -0700

Description

Alastair Florence and colleagues report on the use of an automatic reactor platform from ChemSpeed (1) to accelerate the crystallization of organic molecules (2):

The principal gain over manual crystallization stems from the fact that automation enhances productivity, allowing the search for physical forms to be conducted systematically and reproducibly over a finer grid (e.g. larger solvent library) than might be accessible manually, increasing the probability of observing new forms. In practise, making due time allowance for set-up, sample retrieval and cleaning between experiments, experience has shown that 32 crystallizations per working day is sustainable. Further opportunity for productivity enhancement comes from integration of the platform control PC with an electronic laboratory information management system (LIMS) to provide effective archival, search and retrieval facilities for the recorded control parameters associated with large numbers of crystallizations.

Although High Throughput Synthesis (HTS) has become an integral part of the drug discovery process in companies, larger libraries generally come at a cost to purity and full product characterization.(3) There is still much room available to adopt more automation to the practice of organic chemistry, especially in academic labs. Koppitz and Eis predict (3):

Aside from the technology, we are now entering an era in which chemists working in AMC (Automated Medicinal Chemistry) will probably become more chemistry-oriented than they have been in the past decade, when the focus was on developing and implementing a robust and reliable technology platform. In this context, more chemistry related challenges, such as the discovery and exploitation of new structural motifs in chemical space, development of new chemistries and their application in library synthesis, will hopefully be addressed and solved.

This signal points to more involvement by academia into automating chemistry processes typically done manually. This is significant because there is a greater probability that results will be shared with the scientific community, compared to similar work done in industry.

Eventually it is possible that automation will enable even the open execution of chemistry experiments by leveraging crowdsourcing. (4)

Future of chemistry

Source

1) http://www.chemspeed.com
2) http://www.chemspeed.com/files/publications/sa/pa/2006_crystallization_o...
3) http://www.chemspeed.com/files/publications/sa/pa/2006_Automated%20medic...
4) http://precedings.nature.com/documents/1505/version/1

Aggregating crystallography: overlay journals and new databases

Andrew Walkingshaw — Thu, 01 May 2008 17:17:38 -0700

Description

A lot of science is published every year, and much of it is only available by subscription. That's inflated one of the favourite political footballs among scientists - the whole Open Access debate . It mostly concentrates on the copyright/access status of the journal articles themselves, because they're often perceived to be the major part of scientific output. But that neglects the data which gives rise to the articles, which is often as valuable. In crystallography, this is usually posted on journal websites alongside the papers, but unlike the papers the raw data's uncopyrightable; it's "just" a collection of facts. So it's not subject to the same restrictions as the articles, and you can build new databases by aggregating it.

One example of this kind of thing is CrystalEye, which brings together the latest small-molecule crystallography data, converts it to CML, and puts it up on the web in a more searchable and browsable form. But what it also does is give us a resource we can mine; it exports its data using the Atom protocol, making it easier for informaticists to write new tools to perform analyses over these streams of crystallographic data. In other words, it makes the data more amenable to programming - whether that's machine learning techniques, visualization, social filtering or something new.

The big noise in the Web world, when it comes to open data and the Semantic Web, is the Linking Open Data project. It uses RDF to make very large open datasets - and, as importantly, the links between them - accessible. Through the links, each dataset builds on the previous one, and resources like CrystalEye can be pulled into the cloud; that lets us begin to build new analyses and visualizations of that data, like this map of the global distribution of crystallography papers. As we get more data, and more connected data, then more subtle and complex relationships will be thrown up; and through that we'll get to new science.

Future of chemistry

Source

http://www.prismcoalition.org/
http://en.wikipedia.org/wiki/Open_access
http://wwmm.ch.cam.ac.uk/CrystalEye/
http://inkdroid.org/journal/2007/08/27/linking-open-data/
http://wwmm.ch.cam.ac.uk/blogs/walkingshaw/?p=53