Summarizing the month in European dealmaking.

Current in vivo and in vitro models can't keep up with the demand for the safety assessment of large numbers of compounds emerging from high-throughput strategies. Pharmaceutical companies and start-ups are therefore building new systems that they hope will be capable of predicting the toxicity liabilities of new compounds. Cheminformatics can help week out toxic compounds at the lead selection and optimization stage; toxicogenomics may provide a toxicity diagnostic capability at all stages of drug development. For both toxicology approaches, there is not yet enough high quality data to build predictive models. Toxicology-focused cheminformatics programs attempt to consolidate data from hitherto untapped sources; toxicogenomics companies are engaged in the fussy and expensive process of manufacturing data from scratch and validating them with biological experiments.

Molecular Mining Corp. bills itself as an expert in KDD or "knowledge discovery and data mining." The company's first offering will likely address gene expressoin data. Scientists believe that proper statistical analysis of the data should reveal genes that are co-regulated, and possibly even entire regulatory pathways.

Data mining is the neologism given to the tools of pattern recognition and discovery, themselves a grab bag of mathematical methods used to glean information from complex or noisy data sets. If the number of start-ups like Silicon Genetics is any measure, the pharmaceutical industry looks like data mining's next big market.