  The yield of a chemical reaction quantifies the percentage of the target
product formed in relation to the reactants consumed during the chemical
reaction. Accurate yield prediction can guide chemists toward selecting
high-yield reactions during synthesis planning, offering valuable insights
before dedicating time and resources to wet lab experiments. While recent
advancements in yield prediction have led to overall performance improvement
across the entire yield range, an open challenge remains in enhancing
predictions for high-yield reactions, which are of greater concern to chemists.
In this paper, we argue that the performance gap in high-yield predictions
results from the imbalanced distribution of real-world data skewed towards
low-yield reactions, often due to unreacted starting materials and inherent
ambiguities in the reaction processes. Despite this data imbalance, existing
yield prediction methods continue to treat different yield ranges equally,
assuming a balanced training distribution. Through extensive experiments on
three real-world yield prediction datasets, we emphasize the urgent need to
reframe reaction yield prediction as an imbalanced regression problem. Finally,
we demonstrate that incorporating simple cost-sensitive re-weighting methods
can significantly enhance the performance of yield prediction models on
underrepresented high-yield regions.
