Multistep synthetic routes to eight structurally diverse and medicinally relevant targets were planned autonomously by the Chematica computer program, which combines expert chemical knowledge with network-search and artificialintelligence algorithms. All of the proposed syntheses were successfully executed in the laboratory and offer substantial yield improvements and cost savings over previous approaches or provide the first documented route to a given target. These results provide the long-awaited validation of a computer program in practically relevant synthetic design.
Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer’s help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible “intermediate” atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and – above all – machine extraction of reaction rules underlying modern synthesis-planning programs.
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