We also compared the predictive performance of the model trained on random splits of the full NiCOlit and on NiCOlit restricted to a specific coupling partner or substrate categories with two baselines. When confronted with a novel reaction with unknown yield, the Nearest Neighbor baseline (NN baseline) searches for the closest reaction in the database with a known yield and return its value as the yield prediction for the query reaction. This baseline assesses the performances of one of the natural strategy to extrapolate an unseen yield from the closest known reaction, that mimics the way a human chemist could proceed. The second baseline (scope/optimization baseline) returns the mean yield of reactions extracted from scope (resp. optimization) tables in the database if the query reaction is itself extracted from a scope (resp. optimization) table. This baseline accounts for the fact that a stratification by scope/optimization explains a part of the variability in yields observed within the database. As recent work suggests machine learning doesn't outperform simple baselines in related prediction tasks 5 , our goal was to make sure that there was clear added value from using the machine learning approach. For substrates or coupling partners types with more than 70 data points, the machine learning approach systematically outperformed both baselines.