Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes
  through Reaction Template Sequence Analysis
Junren Li Lei Fang Jian-Guang Lou
Computer-assisted methods have emerged as valuable tools for retrosynthesis
analysis. However, quantifying the plausibility of generated retrosynthesis
routes remains a challenging task. We introduce Retro-BLEU, a statistical
metric adapted from the well-established BLEU score in machine translation, to
evaluate the plausibility of retrosynthesis routes based on reaction template
sequences analysis. We demonstrate the effectiveness of Retro-BLEU by applying
it to a diverse set of retrosynthesis routes generated by state-of-the-art
algorithms and compare the performance with other evaluation metrics. The
results show that Retro-BLEU is capable of differentiating between plausible
and implausible routes. Furthermore, we provide insights into the strengths and
weaknesses of Retro-BLEU, paving the way for future developments and
improvements in this field.