Dark Lab or Smart Lab:  The Challenges for 21st Century Laboratory Software

Abstract

A personal view of some of the challenges for modern laboratory software is described together with some of the techniques that can be used to solve them. New techniques to investigate and to understand the requirements in different laboratory settings are explored, as well as the use of semantic knowledge technologies coupled to pervasive and grid systems to create the necessary support for collaborative chemical laboratory investigations. IntroductionChemistry and the Chemical Sciences have always made extensive use of the developing computing and information technologies and have been avid consumers of available computing power. The uses of this technology include activities such as modelling, simulation, and chemical structure interpretation. New procedures in chemical synthesis and characterisation, particularly in the arena of parallel and combinatorial methodologies, have generated ever-increasing demands on both computational chemistry and computer technology. Currently, the way in which networked services are being conceived to assist collaborative research pushes well beyond the traditional computational chemistry programmes, towards the basic issue of handling chemical information and knowledge (see Figure 1). The rate at which new chemical data can now be generated using increased automation such as combinatorial and parallel synthesis, combined with high throughput screening processes, means that the data can only realistically be handled efficiently by increased automation of the data collection and analysis. Nevertheless, automation is not the answer to all of chemistry, and the need to integrate people and equipment is paramount in most laboratory situations.

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