Computer‐aided synthesis design: 40 years on

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Planning chemical syntheses with deep neural networks and symbolic AI (2017, background): “[14, 13] However, we recently showed that deep neural networks trained on large reaction datasets are capable of ranking extracted symbolic transformations, and can learn to avoid reactivity conﬂicts.”
The future of molecular dynamics simulations in drug discovery (2011, background): “…ligands would be penalized (but not eliminated) for falling outside of known molecular descriptor bounds [86, 87]; transformations that increase molecular diversity could be favored; and the growing process would be completely synthetically ‘‘aware’’ [88–90]—synthetic tractability must be a given.”
A robotic platform for flow synthesis of organic compounds informed by AI planning (2019, methodology): “Computer-aided synthesis planning (CASP) originated as a tool to help chemists identify pathways before executing themin the laboratory ( 25 , 26 ).”
A graph-convolutional neural network model for the prediction of chemical reactivity (2018, background): “There is a rich history of computer-assistance in chemical synthesis [10, 38, 12] including this task of reaction prediction.”