Machine learning (ML) algorithms learn complex functions from data. As it can leverage existing data to perform in silico approximations of costly experimental processes, ML applications have sparked strong interest in chemical sciences. While ML has already made a significant impact in drug development , synthetizability assessment of small molecules or Computer Aided Synthesis Planning 4 , the ability of ML to predict a reaction yield from its experimental conditions remains a major challenge that is intensively studied . Advances on reaction yield prediction would have a major impact on organic synthesis by significantly reducing cost, time and resources necessary to synthesize new chemicals.