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.
authors: Katsuhiko Ishiguro Kazuya Ujihara Ryohto Sawada Hirotaka Akita Masaaki Kotera
hIndex: Masaaki Kotera 21