Backbone Diversity Analysis in Catalyst Design

We present a computer-based heuristic framework for designing libraries of homogeneous catalysts. In this approach, a set of given bidentate ligand-metal complexes is disassembled into key substructures (building blocks). These include metal atoms, ligating groups, backbone groups, and residue groups. The computer then rearranges these building blocks into a new library of virtual catalysts. We then tackle the practical problem of choosing a diverse subset of catalysts from this library for actual synthesis and testing. This is not trivial, since catalyst diversity itself is a vague concept. Thus, we first define and quantify this diversity as the difference between key structural parameters (descriptors) of the catalysts, for the specific reaction at hand. Subsequently, we propose a method for choosing diverse sets of catalysts based on catalyst backbone selection, using weighted D-optimal design. The computer selects catalysts with different backbones, where the difference is measured as a distance in the descriptors space. We show that choosing such a D-optimal subset of backbones gives more diversity than a simple random sampling. The results are demonstrated experimentally in the nickel-catalysed hydrocyanation of 3-pentenenitrile to adiponitrile. Finally, the connection between backbone diversity and catalyst diversity, and the implications towards in silico catalysis design are discussed