A framework for reviewing the results of automated conversion of structured organic synthesis procedures from the literature

Abstract

Organic synthesis procedures in the scientific literature are typically shared in prose (i.e., as unstructured data), which is not suitable for data-driven research applications. To represent such procedures, there is a well-structured language, named chemical description language (χDL). While automated conversion methods from text to χDL using either a rule-based approach or a generative large language model (GLLM) have been proposed, they sometimes produce errors. Therefore, human review following an automated conversion is essential to obtain an accurate χDL. The aim of this work is to visualize embedded information in the original text with a structured format to support the understanding of human reviewers. In this paper, we propose a novel framework for editing automatically converted χDLs from the literature with annotated text. In addition, we introduce a rule-based conversion method. To improve the quality of automated conversions, a method of using two candidate χDLs with different characteristics was proposed: one generated by the proposed rule-based method and the other by an existing GLLM-based method. In an experiment involving six organic synthesis procedures, we confirmed that showing the outputs of both systems to the user improved recall compared with showing one output individually.