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Graph mining techniques for analyzing large collections of molecules to find regularity or patterns among molecules of a specific class, such as finding common properties in large numbers of drug candidates, finding molecular features that inhibit the desired reaction etc. is an important research issue in bioinformatics as well as molecular informatics. In this context, finding frequent graphs has received increasing attention over the past years. But, the computational complexity of the underlying problem and the large amount of data to be explored essentially render traditional sequential algorithms practically useless. To address such problems a distributed algorithm is adopted to find the frequent sub-graphs and to discover interesting patterns in molecular compounds. However, this problem is characterized by a highly irregular search tree, whereby reliable workload prediction is very hard. Therefore, a genetic algorithm (GA) is proposed to solve the dynamic load-balancing problem of highly irregular search tree.