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The ‘Wired’ Universe of Organic Chemistry

Network theory shows promising insight into the future of organic chemistry and the ability to optimize reaction pathways in order to reduce costs. By considering molecules to be nodes and reactions the arrows connecting them, a complex network forms, giving rise to apparent trends that previously went unnoticed. With three distinct regions, including a core, periphery, and islands, this chemistry network can be analyzed by considering the properties of each region and their associated trends. The core, a strongly connected component in which a synthetic path exists between any two molecules, contains structurally diverse and relatively small, and therefore more reactive, molecules vital to to the synthesis of other organic compounds. Considering an optimized set of 300 of the core molecules, these alone give rise to around 78% of known organic molecules. These molecules reachable from the core constitute the periphery, most of which can reach the core in a maximum of 7 steps, showing relation to the concept of Six Degrees of Separation describing the network of human relations.

Further analysis shows that more densely connected molecules tend to also be cheaper, providing insight into ways to lower costs of molecular synthesis. Because they are so widely used in the creation of other products and also the cheapest, molecules with higher cluster density are considered the most useful to companies. In contrast, molecules that make up the islands, unconnected to the giant component, compose most of the difficult synthetic targets of the chemistry world. Other connections to network theory can be seen in the existence of “hub” molecules, cheaper and more densely connected molecules resulting from higher reactivity with other substances. Using the known possible reaction pathways as arrows leading to a certain product, industries can use analysis of this network to discover which substrates and paths result in minimized costs of production. Other possible uses arise in considering groups of nodes that composes certain structural groups and how groups travel through the network, with more uses becoming possible with improved algorithms to analyze this data as the network evolves.

http://www.nature.com/nchem/journal/v1/n1/full/nchem.136.html

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