In this paper we propose a novel graph-based genetic algorithm for the evolution of novel molecular graphs from a predefined set of elements or molecular fragments with an external objective function. A brief overview of existing genetic algorithm approaches in molecular design is provided followed by a description of our approach. The paper continues to suggest a novel application of this program to the multiobjective evolution of median molecules that are structurally representative of a set of objective molecules. We conclude with a summary of our initial results along with a discussion of a variety of improvements and applications of our approach.

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Catalytic conversion of biomass is a key challenge that we chemists face in the twenty-first century. Worldwide, research is conducted into obtaining bulk chemicals, polymers and fuels. Our project centres on glucose valorisation via furfural derivatives using catalytic hydrogenation. We present here new results for a set of 48 bimetallic catalysts supported on silica, and demonstrate the application of data mining tools to identify major trends in the data. These results are combined with a full factorial data set for the hydrogenation of 5-ethoxymethylfurfural over alumina-supported transition metal catalysts. All the catalysts in the combined datasets were synthesized and tested for performance under identical conditions. This, combined with the fact that no combinations of metals were left out, enables the use of advanced data mining tools. The paper describes the data and highlights the relevant trends from a chemist's viewpoint.

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