In organic chemistry, we are currently witnessing a rise in artificial intelligence (AI) approaches, which show great potential for improving molecular design, facilitating synthesis and accelerating the discovery of novel molecules. Based on an analogy between written language and organic chemistry, Philippe et al. built linguistics-inspired transformer neural network models for chemical reaction prediction, synthesis planning, and the prediction of experimental actions. They extended the models to chemical reaction classification and fingerprints. By finding a mapping from discrete reactions to continuous vectors, they enabled efficient chemical reaction space exploration. Moreover, the group specialized similar models for reaction yield predictions. Intrigued by the remarkable performance of chemical language models, they discovered that the models can capture how atoms rearrange during a reaction, without supervision or human labelling, leading to the development of the open-source atom-mapping tool RXNMapper. These advances led to the developments of IBM RXN for Chemistry and RoboRXN, the prototype of an AI-driven, cloud-connected, and automated synthesis platform. RoboRXN will enable chemists to execute chemical syntheses from the comfort of their home. Philippe will provide an overview of the different contributions that are at the base of this digital synthetic chemistry revolution.
Image taken from: http://rxnmapper.ai/index.html
