Doc2SAR: A Synergistic Framework for High-Fidelity Extraction of Structure-Activity Relationships from Scientific Documents

TL;DR

Doc2SAR is a new framework that combines domain-specific tools and fine-tuned multimodal language models to accurately extract structure-activity relationships from scientific documents, overcoming previous limitations.

Contribution

It introduces Doc2SAR, a synergistic approach integrating specialized tools with enhanced language models, and provides DocSAR-200, a benchmark for evaluating SAR extraction methods.

Findings

Achieves 80.78% Table Recall on DocSAR-200

Outperforms GPT-4o by 51.48% in Table Recall

Demonstrates practical usability with efficient inference and a web app

Abstract

Extracting molecular structure-activity relationships (SARs) from scientific literature and patents is essential for drug discovery and materials research. However, this task remains challenging due to heterogeneous document formats and limitations of existing methods. Specifically, rule-based approaches relying on rigid templates fail to generalize across diverse document layouts, while general-purpose multimodal large language models (MLLMs) lack sufficient accuracy and reliability for specialized tasks, such as layout detection and optical chemical structure recognition (OCSR). To address these challenges, we introduce DocSAR-200, a rigorously annotated benchmark of 200 scientific documents designed specifically for evaluating SAR extraction methods. Additionally, we propose Doc2SAR, a novel synergistic framework that integrates domain-specific tools with MLLMs enhanced via supervised fine-tuning (SFT). Extensive experiments demonstrate that Doc2SAR achieves state-of-the-art performance across various document types, significantly outperforming leading end-to-end baselines. Specifically, Doc2SAR attains an overall Table Recall of 80.78% on DocSAR-200, exceeding end2end GPT-4o by 51.48%. Furthermore, Doc2SAR demonstrates practical usability through efficient inference and is accompanied by a web app.