Machine learning of strategic knowledge in organic synthesis from reaction databases

Perception of strategic bonds is a very important step in organic synthesis planning. We describe a method that can help to identify such bonds in a target molecule. This method is based on recognition of similarities between a new synthetic problem and earlier solved ones stored in a reaction database. A test of performance of the implemented program is presented.