Elsevier, LG AI Research Integrate Chemistry-Specific AI Vision
By Bio-IT World Staff
September 22, 2026 | Elsevier and LG AI Research last week announced image search capabilities to flag visual chemistry in patents and scientific literature. Chemistry-specific AI vision technology developed by LG AI Research is being used within Elsevier’s content extraction and curation processes for Reaxys, Elsevier’s discovery chemistry solution. Substance information from images in patent and journal content is captured far more quickly, accurately, and at far greater scale than was previously possible.
Much of the substance and reaction information chemists rely on is communicated through figures, drawings, and reaction schemes rather than text. Making that chemistry discoverable at scale is a specialist AI challenge, the companies said. A model that misreads a bond may identify the wrong compound; one that misses a structure gives chemists an incomplete picture. This matters most in areas such as novelty searching, competitive intelligence and synthesis planning, and in inorganic and organometallic chemistry, where complex structures are harder to extract and index.
The novel chemistry-specific AI vision technology from LG AI Research combines molecule detection, reaction-diagram parsing and optical chemical structure recognition (OCSR) in a single model. Called MolMole withing LG AI Research, the chemistry model is part of LG AI Research’s Deep Document Understanding (DDU) technology. According to LG AI Research’s published benchmarking, its tool outperforms alternatives at extracting chemistry from a full document page.
“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,” the LG team wrote. “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.”
Elsevier Integration
Elsevier’s Reaxys combines connected chemistry data with precision AI and expert curation, so chemists, materials scientists and engineers have trusted evidence to identify and prioritize the most promising compounds; accelerate discovery with traceable, AI-powered insights; and design scalable, sustainable synthesis routes.
Elsevier further validated the LG AI extraction pipelines against existing Reaxys benchmarks so accuracy is not traded for scale. The full pipeline has undergone a rigorous period of testing across Elsevier’s data and workflow tools to ensure accuracy and quality before wider use.
“Every hour a chemist spends deciphering figures or images to see what has already been made is an hour that could instead be spent on chemistry discovery,” said Mirit Eldor, managing director, life sciences, Elsevier, in a press release. “Our partnership with LG AI Research gives that time back, lifting more chemistry out of the image and into Reaxys—curated, searchable and ready to act on. A structure buried in a figure should be evidence rather than a dead end.”
In the same press release, Hwayoung Edward Lee, lead of the AI Biz Transformation Unit at LG AI Research, added: “Through our integration with Elsevier, we are proving that advanced chemistry-specific AI can seamlessly convert raw visual data into structured knowledge for global researchers.”
The collaboration is ongoing and reaction extraction will be the next stage, extending image-based extraction beyond individual substances to broaden the reaction evidence available through Reaxys. Elsevier and LG AI Research are also exploring further customer challenges to tackle together, bringing together LG AI Research’s specialist AI capabilities with Elsevier’s chemistry content, scientific expertise and curation.