# Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective

arXiv: https://arxiv.org/abs/2402.05971 · HTML: https://mlsanity.com/paper/2402.05971/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression](https://mlsanity.com/paper/2205.15236/) (2022)
- [Semi-Supervised Graph Imbalanced Regression](https://mlsanity.com/paper/2305.12087/) (2023)

## Built on by (most cited first, 6 of 6)

- [SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression](https://mlsanity.com/paper/2504.21152/) (2025)
- [ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery](https://mlsanity.com/paper/2506.11041/) (2025)
- [log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling](https://mlsanity.com/paper/2411.03320/) (2024)
- [Spectral Manifold Harmonization for Graph Imbalanced Regression](https://mlsanity.com/paper/2507.01132/) (2025)
- [Regression Augmentation With Data-Driven Segmentation](https://mlsanity.com/paper/2508.01455/) (2025)
- [Role-Aware Morgan Fingerprints for Reaction Yield Prediction](https://mlsanity.com/paper/2609.22167/) (2026)

## Abstract (arXiv)

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.