"With today's technology, at best only 30% of multi-omics (Multi-Omics) data can be interpreted by humans. The data structures are complex, and Korea lacks specialized talent. We can break through these limits with artificial intelligence (AI) and cloud automation."
Multi-omics, which integrates analysis of the genome, transcriptome, and proteome, is considered a key pillar of new drug development and personalized precision medicine. But because the data are complex, integrated analysis has been difficult; as AI technology advances, the industry's approach is changing.
Lee Nam-yong, head of Cellkey AI, gave a lecture on "Redesigning the bio R&D workflow with AI" at the "2026 SME AX (AI transition) Leaders Forum," hosted by ChosunBiz at the Westin Josun Hotel in Jung District, Seoul, on the 9th.
Founded in 2021, Cellkey AI is a bio-technology company that combines AI and cloud automation to build an AI-based precision medicine platform. It has algorithmic technology that can efficiently analyze more than 1 million proteins and glycoproteins in the human body. It is currently pursuing joint research and business through open innovation with global research institutes and large corporations in the United States, Japan, the United Kingdom, the Middle East, and Germany.
Lee cited data fragmentation as the biggest problem in current bio research settings.
Research institutions typically outsource analyses to different vendors, receive results by email, and store them on individual researchers' PCs. When a researcher leaves, data use is cut off, and no standard data for AI training are accumulated.
The company developed a multi-omics platform that combines AI and cloud automation to address these limits. It is the agent AI-based bio R&D platform Omics Farm. When a researcher requests an analysis on the platform, it connects with partner research institutions to generate data, then standardizes and assets them for AI training.
Lee said, "Omics Farm is an automation platform that covers the entire process from multi-omics data generation, analysis, learning, and interpretation to automatic report creation," adding, "Our goal is to build a proprietary 'Sovereign Multi-Omics Foundation Model' based on accumulated data."
The company is also developing a Deep Learning model that integrates genome, transcriptome, and protein data to predict biomarkers and drug candidates. It also built BioEOS, a bio-specialized vertical AI agent that automatically analyzes papers and patents.
AI is driving process innovation in biopharmaceutical CDMO (contract development and manufacturing). Bio manufacturing remains rooted in traditional methods and is considered a representative "green field" with slow digital transformation.
Cellkey AI moved away from the conventional cell development method of randomly searching for gene insertion sites, enabling an AI agent to collect and analyze vast papers and patents to identify optimal "hotspot" regions.
In purification processes that remove impurities, instead of repeating dozens of physical experiments, it uses an AI-based "surrogate model" to derive optimal experimental conditions in a simulation environment.
Lee said, "With this approach, we shortened purification processes that used to take months to a few weeks, and we also achieved a sharp reduction in expense by 70%–80%."
An innovation case in a cell analysis process for animal alternative experiments was also shared. Previously, researchers faced a roughly three-week bottleneck as they checked each cell's growth and ratios by looking through an electron microscope.
AI is also applied to reading cell images. Lee said, "It used to take about three weeks for a researcher to check 96 cell images one by one with an electron microscope, but we enabled an AI agent to analyze the images and automatically generate a quantified report," adding, "We achieved more than a 3,000% improvement in work efficiency."
Lee named "data," "automation," and "physical AI" as the three pillars of future bio innovation.
He said, "Automation experiments with robots produce high-quality data, and these data train AI to efficiently redesign the next experiment. We must create a 'virtuous cycle where data grow data' to secure global competitiveness."
An automated research facility of the Chinese AI drug discovery corporation Insilico Medicine was introduced as a case. Lee said, "In the near future, the convergence of physical AI and bio will become a reality in Korea as well."