August 31, 2009 (FinancialWire) — Symyx Technologies, Inc. (NASDAQ: SMMX) announced a new release of the Symyx Isentris(r) decision support system. Isentris 3.2 enables scientists to explore, compare and report on information spanning multiple experiments captured in electronic lab notebooks, the Symyx Lab Execution and Analysis  software suite, laboratory information management systems  and other information management systems.

Symyx Isentris is the cross-experiment data access, analysis and reporting tool for any LIMS system — driving better informed decisions in scientific experiments and studies. This latest Isentris release supports the ongoing collaboration between Symyx and Thermo Fisher Scientific aimed at integrating Symyx Notebook and Symyx Isentris software with Thermo Scientific’s industry-leading LIMS including Watson LIMS used in DMPK/bioanalytical research.

“The Isentris 3.2 release with LEA integration is a significant milestone in the Symyx vision for an integrated electronic laboratory environment, encompassing LIMS capabilities with advanced analytics and reporting for a broad range of applications,” said Dr. Trevor Heritage, president of Symyx’s software business unit. “The cross-experiment analysis and reporting capabilities of Isentris 3.2 significantly increase the value of information captured within LIMS for researchers working in discovery pharma, in analytical, formulations, and process chemistry — even in agricultural chemistry, chemicals, energy, and consumer goods.”

Isentris 3.2 significantly extends the current analysis and reporting capabilities of Symyx LEA software. For many researchers, Microsoft Excel software is the analysis tool of choice. Isentris now enables LEA researchers to easily aggregate data from multiple information sources in a single view and to report on chemical structures, images, chromatograms, spectra and XY datasets, all within the familiar Excel environment.

With the Isentris software developer kit, R&D organizations can extend existing reporting functionality with cross-experiment analysis and reporting, thereby combining additional information sources into analyses and reports on an experiment-by-experiment or study-by-study basis. Scientists can rapidly validate findings by comparing experimental results. They can improve the design of future experiments through comparisons with past experiments and make better decisions by combining information from disparate sources.

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