Release of MolMole, an AI That Understands Chemical Molecular Structural Formula Information from…

Release of MolMole, an AI That Understands Chemical Molecular Structural Formula Information from Documents | by LG AI Research | Medium

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Release of MolMole, an AI That Understands Chemical Molecular Structural Formula Information from Documents

LG AI Research

7 min read

Jun 12, 2025

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We encounter tons of documents every day. We make sense of the information in these documents, including the diagrams, images, and graphs used alongside the text, and use them to understand the deeper meaning behind the document.

“If AI could understand different forms of data in documents, how would our lives change?”

LG has been researching Deep Document Understanding (DDU) technology with the goal of creating an AI that understands every document in the world. LG’s DDU technology not only understands text, graphs, and tables in general documents, but also complex molecular structural formulas and reaction formulas in chemical papers and patents.

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LG’s DDU Technology

We’re excited to unveil the latest achievement of LG’s Deep Documnet Understanding (DDU) technology — a specialized model for the field of chemistry, MolMole. The MolMole model has demonstrated outstanding performance by achieving SOTA against major competing models. The technical report of the MolMole model provides a technical description of the model and performance evaluation results.

Along with the publication of this research, LG has also built our own benchmark dataset to measure the performance of DDU technology in the chemical field. Until now, there have been benchmarks to evaluate the performance of single models such as Optical Chemical Structure Recognition (OCSR), but there has been no benchmark to evaluate the performance of extracting molecular structural formulas from full PDF documents in the context of real-world chemists. We wanted to build a benchmark to measure the performance of our models to activate the AI ecosystem, and we will be releasing our own benchmark dataset later this year. We hope that our attempt will spark discussions and technical exchanges among researchers of DDU technologies.

MolMole Technical Report →

MolMole on GitHub →

MolMole, DDU Specialized for the Chemical Field

Chemical patents and papers contain tons of molecular structural formula information, but most of it has not been converted into machine-understandable data. What would happen if AI could recognize and convert molecular structural structures in chemical documents into data?

First and foremost, a large database of molecular structural formulas can be built, and researchers can search for chemical reactions in the database and perform the large-scale analysis required for their research. AI technology can help chemists work more efficiently and support breakthrough discoveries in chemistry.

High difficulty of molecular structural formula recognition technologyExtracting molecular structure and reaction data from complex documents is challenging, as patents and papers contain chemical information in many different forms and have unstructured features that make it impossible to define rules. The layout of the documents is also complex, making it very challenging to develop AI to recognize them accurately.

MolMole’s unique features and technologies An important feature of MolMole is that it has been implemented in a framework that reflects the needs of real-world researchers. Existing DDU technologies in the chemical field are limited to converting molecular structural formula images as input values. However, in the real world of research, patents, papers, and other materials exist in the form of PDF documents. Other AI models that require images in PDF documents to be cropped and entered as input values are obviously less useful. Therefore, MolMole was implemented so that PDF documents can be entered as input values as they are. When you input a PDF document into MolMole, it recognizes and provides chemical data such as molecular structures at once.

The MolMole model consists of three modules: ViDetect, ViReact, and ViMore, which are responsible for extracting molecular structural formula domains from documents and recognizing the composition of molecular structures and chemical reactions.

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MolMole model pipeline

1. Convert molecular structures into SMILES, InChI, and Mol formatsThe ViMore module recognizes molecular structures within documents and analyzes the atoms and their bonding relationships to convert them into standard chemical representations such as SMILES, InChI, and Mol formats. Given that missing even a single atom or bond can lead to an entirely different molecular interpretation, the module is engineered to achieve a high level of precision.Real-world constraints were also carefully considered during development. Patent documents, for instance, are often available only as scanned images, which can introduce significant noise and hinder recognition accuracy. ViMore addresses this challenge with specialized techniques designed to improve performance in noisy, scan-based document environments. 2. ViReact: Extracting structured response information from documents Chemical reaction diagrams typically consist of three key components: reactants, reaction conditions, and products. The ViReact module is designed to identify the position of each component within a reaction diagram and assign the appropriate classification to each region. Given that reaction diagrams can range from simple single-line formats to more complex tree and graph structures, accurately extracting this information poses a significant technical challenge. ViReact addresses this with a robust approach to structured information extraction, enabling precise parsing of a wide variety of reaction formats. 3. ViDetect: Extracting molecular structure domains from documentsThe ViDetect module plays a critical role in precisely detecting molecular structure regions within PDF documents by identifying them with bounding boxes. Unlike general object detection tasks, even the omission of a single atom can result in an entirely different molecule — demanding exceptionally high precision and accuracy. To meet this challenge, ViDetect leverages advanced object detection techniques to ensure every component of a molecular structure is…