Talk Abstract #1

October 11-12, 2005
eCheminfo & Innovation Well Conferences

An interactive environment for knowledge management

James H Wikel, Coalesix Inc., Cambridge, MA

The challenge of developing useful drugs from active molecules is a daunting problem. It is a complex task requiring the simultaneous satisfaction of many criteria including: inherent potency, low side effect profile, good solubility, oral absorption, appropriate distribution in various tissues including the target tissue, metabolism at a rate that ensures sufficient residence time in vivo to be an effective drug, and excretion of parent drug and metabolites in order to minimize any toxic accumulation. The pharmaceutical industry has collected data in these areas for decades. In some cases, the analysis of these data has provided sufficient information to overcome a challenge with one or more of these criteria. The evaluation of these important properties generally occurs after potency is nearly maximized and then only the most potent compounds in the SAR are subjected to these studies. Effective knowledge management from these multidimensional data combined with the expert intuition of the scientist is a challenge for the industry in order to increase the efficiency of drug discovery. We have developed a novel optimization technology focused on providing an in silico environment enabling and synergizing multiparameter optimization with expert intuition and knowledge. The resulting environment enables the identification of a diverse selection of preclinical drug candidate molecules with better overall properties, thus supporting better decision making and providing a higher likelihood of clinical success.

Several components of the knowledge management problem - such as synthetic tractability or IP status – are subjective or inherently implicit and so do not lend themselves to a purely computational approach. Coalesix's Candidate Design Environment is based on Interactive Evolutionary Computing (IEC). IEC is an optimization technique, which uses subjective human evaluation in problems where it is difficult or impossible to design a fitness function for automatic search of the input space.


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