Title: Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis. Abstract: Retrosynthesis is a problem to infer reactant compounds to synthesize a given
product compound through chemical reactions. Recent studies on retrosynthesis
focus on proposing more sophisticated prediction models, but the dataset to feed
the models also plays an essential role in achieving the best generalizing models.
Generally, a dataset that is best suited for a specific task tends to be small. In
such a case, it is the standard solution to transfer knowledge from a large or
clean dataset in the same domain. In this paper, we conduct a systematic and
intensive examination of data transfer approaches on end-to-end generative models,
in application to retrosynthesis. Experimental results show that typical data transfer
methods can improve test prediction scores of an off-the-shelf Transformer baseline
model. Especially, the pre-training plus fine-tuning approach boosts the accuracy
scores of the baseline, achieving the new state-of-the-art. In addition, we conduct a
manual inspection for the erroneous prediction results. The inspection shows that
the pre-training plus fine-tuning models can generate chemically appropriate or
sensible proposals in almost all cases.