我们取得了很大进展吗？从不平衡回归视角重新审视化学反应产率预测
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
AI总结：
本文指出化学反应产率预测中高产率区间性能不足源于数据分布偏向低产率的不平衡问题，并通过实验证明将产率预测重构为不平衡回归任务并采用成本敏感重加权方法可显著提升高产率区间的预测性能。
摘要：
化学反应的产率量化了在化学反应过程中生成的目标产物相对于所消耗反应物的百分比。准确的产率预测可以指导化学家在合成规划中选择高产率反应，从而在投入时间和资源进行湿实验室实验之前提供有价值的见解。尽管近年来产率预测的进展带来了整个产率范围内整体性能的提升，但一个尚未解决的挑战仍然存在：如何改进对化学家更为关注的高产率反应的预测。在本文中，我们认为高产率预测中的性能差距源于现实世界数据的不平衡分布，该分布偏向低产率反应，这通常是由未反应起始原料和反应过程中固有的不确定性造成的。尽管存在这种数据不平衡，现有的产率预测方法仍然平等对待不同的产率区间，假设训练分布是平衡的。通过在三个真实世界产率预测数据集上的大量实验，我们强调了将反应产率预测重新构建为不平衡回归问题的迫切需要。最后，我们证明，引入简单的成本敏感重加权方法可以显著提升产率预测模型在代表性不足的高产率区间上的性能。
英文摘要：
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.
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