Drug discovery: exploring the utility of cluster oriented genetic algorithms in virtual library design

In silico combinatorial library design involves the identification of molecules that have a greater probability of exhibiting desired biological activity when subjected to in vitro screening (assaying) against a particular biological target. The paper introduces the integration of cluster-oriented genetic algorithms (COGAs) with such machine-based library design environments. COGAs have a proven capability to identify high-performance regions of complex, continuous design spaces relating to engineering design problems. Modifications to the basic COCA approach are described that allow a transfer of this capability from continuous variable parameter space to the highly discrete spaces described by reactants across reagent libraries. Results relating firstly to the identification of optimal molecules and secondly to the focussing of reagent libraries in terms of high-performance reactants are presented. Single objective optimisation and focussing are initially considered before moving on to multiple objective satisfaction