ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data

2025-06-30

AI Summary

Automated extraction of chemical synthesis steps is hindered by textual ambiguity in scientific literature and the scarcity of high-quality annotated data. To address this, we propose ChemActor—a novel foundation model that achieves end-to-end, precise parsing of unstructured experimental text into machine-executable operation sequences. Methodologically, ChemActor integrates distribution-aware data filtering, a large-language-model-based multi-round iterative review mechanism, and a two-stage task learning paradigm (reaction → description → action), coupled with full-parameter fine-tuning and explicit modeling of machine-executable actions. On the Reaction-to-Description (R2D) and Description-to-Action (D2A) benchmarks—two core tasks for synthesis procedure understanding—ChemActor outperforms prior state-of-the-art methods by 10% absolute gain, substantially improving operational identification accuracy and procedural executability. This establishes ChemActor as a robust foundation model for automating organic synthesis workflows.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experimental procedures and structured action sequences. We propose a sequential LLM-generated data framework to address the challenges of insufficient and low-quality annotated data. This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input. Additionally, we introduce a novel multi-round LLMs circle review metric, which reflects the model's advanced understanding of chemical experimental procedures. Extensive experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor, augmented by LLM-generated data, achieves state-of-the-art performance, outperforming the baseline model by 10%. The code is available at: https://github.com/Zhanghahah/ChemActor.

Problem

• Automated extraction of chemical procedures from literature
• Addressing ambiguity in chemical language and annotation costs
• Generating machine-executable actions from molecule inputs

Innovation

• Fully fine-tuned LLM for chemical action conversion
• Sequential LLM-generated data framework for annotation
• Multi-round LLMs circle review metric