Retrosynthesis prediction enhanced by in-silico reaction data
  augmentation
Xu Zhang Yiming Mo Wenguan Wang Yi Yang
Recent advances in machine learning (ML) have expedited retrosynthesis
research by assisting chemists to design experiments more efficiently. However,
all ML-based methods consume substantial amounts of paired training data (i.e.,
chemical reaction: product-reactant(s) pair), which is costly to obtain.
Moreover, companies view reaction data as a valuable asset and restrict the
accessibility to researchers. These issues prevent the creation of more
powerful retrosynthesis models due to their data-driven nature. As a response,
we exploit easy-to-access unpaired data (i.e., one component of
product-reactant(s) pair) for generating in-silico paired data to facilitate
model training. Specifically, we present RetroWISE, a self-boosting framework
that employs a base model inferred from real paired data to perform in-silico
reaction generation and augmentation using unpaired data, ultimately leading to
a superior model. On three benchmark datasets, RetroWISE achieves the best
overall performance against state-of-the-art models (e.g., +8.6% top-1 accuracy
on the USPTO-50K test dataset). Moreover, it consistently improves the
prediction accuracy of rare transformations. These results show that Retro-
WISE overcomes the training bottleneck by in-silico reactions, thereby paving
the way toward more effective ML-based retrosynthesis models.