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An Anonymous Coward writes:

This high-throughput reaction prediction (HTRP) idea has been tried several times before, and the paper provides a useful review of these. Broadly, these attempts have used either some sort of rule-based expert system framework, attempted to work out a logic or grammar of chemical reactivity to extrapolate with, or used outright machine learning techniques.

[...] As a number of other theoretical approaches to organic chemistry have done, they're shifting the world of organic chemistry over into a graph-theory problem. From this perspective, discovering new reactions becomes a search for new nodes and edges in the graph[...] let's say you have two molecules, A and B, and both of them are both known to react with a partner C to give some new product in each case. The program will note all these similarities, and searches for cases where compound A reacts with yet another molecule type D. Since A and B have been classed as having a similar reactivity pattern (they both reacted usefully with C), the program hypothesizes that B will do something with D as well

[...] They tested this approach by using everything up to 2013 to predict reactions, and the set of reactions published since then to check their results. Looking at 180,000 randomly selected reactions, the predictions were correct about 67% of the time.

http://blogs.sciencemag.org/pipeline/archives/2016/08/31/predicting-new-reactions
http://arxiv.org/abs/1608.07117

Original Submission