Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
This addresses a critical issue for chemists in synthesis planning by improving predictions for high-yield reactions, though it is incremental as it applies existing imbalanced regression techniques to this domain.
The paper tackles the problem of inaccurate predictions for high-yield chemical reactions due to imbalanced data skewed toward low yields, and demonstrates that using cost-sensitive re-weighting methods significantly improves performance in high-yield regions.
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.