During tuning, we optimized the number of message-passing steps, the hidden size of the message-passing layers, the number of layers, the hidden size of the feed-forward neural network, and the dropout ratio. Both tuning and production models utilized summation to aggregate atomic features into molecular feature vectors, with the optimized hyperparameters summarized in Table . Furthermore, molecular graphs with explicit hydrogens were used as input for all models to clearly distinguish between keto and enol tautomers when employing the RIGR featurizer.