September 11-14, 2005
An interactive evolutionary design environment for multiparameter optimization
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. We have developed a novel optimization technology focused on providing an in silico environment that 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.
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.
Drug candidate design and optimization is complex conundrum with two key characteristics, both of which are naturally addressed by the strengths of IEC. First, as described above, a successful drug candidate must satisfy a battery of complex and interrelated in vivo potency and ADMET requirements that are difficult, if not impossible, to describe in a simple computer based model fitness function. While a skilled medicinal chemist can navigate these requirements, progress is slow and inefficient. Second, there are several components of the problem - such as synthetic tractability or IP status – that are subjective or inherently implicit and so do not lend themselves to a purely computational approach.
CoalesiX’s Candidate Design Environment uses IEC to leverage both the expertise of the medicinal chemist and the computational skills of the computational chemist as key elements of this evolutionary search process. The CoalesiX Candidate Design Environment provides for the mutual reinforcement between a) the capabilities of computational technologies to handle large amounts of data and processing (computational scientists) and b) the human ability of skilled medicinal chemists to make judgment calls based upon intuition and experience. The environment creates a comfortable forum for the blending of the computational science inherent in computer based models, as well as the intuition and experience of the medicinal chemists. This talk will describe the environment, how the environment fosters productive interaction between the computational and medicinal chemists, and case studies providing validation for the methods.
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