RetroOOD: Understanding Out-of-Distribution Generalization in
  Retrosynthesis Prediction
Yemin Yu Luotian Yuan Ying Wei Hanyu Gao Xinhai Ye Zhihua Wang Fei Wu
Machine learning-assisted retrosynthesis prediction models have been gaining
widespread adoption, though their performances oftentimes degrade significantly
when deployed in real-world applications embracing out-of-distribution (OOD)
molecules or reactions. Despite steady progress on standard benchmarks, our
understanding of existing retrosynthesis prediction models under the premise of
distribution shifts remains stagnant. To this end, we first formally sort out
two types of distribution shifts in retrosynthesis prediction and construct two
groups of benchmark datasets. Next, through comprehensive experiments, we
systematically compare state-of-the-art retrosynthesis prediction models on the
two groups of benchmarks, revealing the limitations of previous in-distribution
evaluation and re-examining the advantages of each model. More remarkably, we
are motivated by the above empirical insights to propose two model-agnostic
techniques that can improve the OOD generalization of arbitrary off-the-shelf
retrosynthesis prediction algorithms. Our preliminary experiments show their
high potential with an average performance improvement of 4.6%, and the
established benchmarks serve as a foothold for further retrosynthesis
prediction research towards OOD generalization.