SyntekaBio CEO, “No AI Drug Leader, a Chance for Us”

by송영두 기자
2025.12.02 14:21:02

[Song Young Doo, Edaily Reporter] “Companies like Schrödinger, Recursion and Insilico Medicine are drawing attention in the global artificial intelligence(AI) drug discovery market, but it is still hard to say there is a true ‘global leader’ in AI-driven drug development. SyntekaBio is a first-generation player in next-generation ‘physics-based AI drug’ discovery, and with our large-scale data center now in place, we plan to accelerate global growth and stand shoulder to shoulder with those companies.”

On the 17th, SyntekaBio CEO Jong-Seon Jung outlines the company’s future growth strategy in an interview with Edaily. (Photo by Young-Doo Song reporter)

In an interview with Edaily on the 17th, Jong-Seon Jung, CEO of SyntekaBio, said changes at home and abroad in the AI drug development environment, combined with the absence of a clear global leader, could become “a window of opportunity” for SyntekaBio to leap forward.

Today, Schrödinger, Recursion, Insilico Medicine and Atomwise are among the firms proving their potential in the global AI drug discovery market. Schrödinger counts Eli Lilly and other major global pharmas as clients, while Recursion, backed by NVIDIA, is known for its AI model–based automated drug discovery platform capable of running thousands of experiments in parallel.

Insilico Medicine and Atomwise are also attracting strong market interest, but among them only Schrödinger and Recursion are publicly listed. Their current market capitalizations stand at around 1.8 trillion won for Schrödinger and 3.2 trillion won for Recursion. Compared with global leaders in other industries, these valuations are still modest relative to their technological potential, and even Schrödinger, the top earner among them, generates only about 300 billion won in annual revenue. Jung noted that this leaves “plenty of room for a latecomer like SyntekaBio to overtake them with innovation and differentiation.”

“Since our IPO in 2019, we have closely analyzed the compound discovery technologies of major players such as Schrödinger and Recursion,” Jung said. “Institutional investors have also run a variety of comparative assessments between SyntekaBio and those companies.” He added, “We believe we understand more than 95% of the technologies behind Schrödinger and Recursion, and if you look strictly at the hit- and lead-finding space, their capabilities are not even half of what SyntekaBio has today.”

He continued, “At the time of our listing, SyntekaBio’s small-molecule discovery platform and automation system were not fully completed, so it was difficult to discuss who had the superior technology versus Schrödinger and others,” adding, “But recently we have combined quantum and molecular dynamics–based physical simulation with deep learning, and on top of that built a precision-medicine target discovery infrastructure anchored by a large-scale data center. Our competitiveness is now in place; what remains is to prove it to the market.”

The essence of AI-driven drug development, he stressed, is not only cutting development timelines and costs but also materially improving clinical success rates. The global market is moving beyond using AI as a mere “support tool” and into a phase where quantum computing and massive data accumulation are leveraged to achieve faster and more accurate target selection. According to Jung, Schrödinger is one of very few companies worldwide that fully embrace quantum and molecular dynamics–based physical simulation, and SyntekaBio is currently the only firm to have built its own large-scale data center for high-throughput candidate discovery across multiple therapeutic areas.

“SyntekaBio’s value positioning is as a first-generation ‘physics-based AI drug’ and ‘genomic big data’ company,” he said. “While other players tend to focus on specific modalities, we have built pipelines that span the full range of major modalities—from antibodies and small molecules to cancer vaccines. In terms of scope, we cover the broadest spectrum.”

He added that the company’s self-built data center is a key differentiator. “We have been the first to fully integrate a data infrastructure that defines which targets to pursue—our genomic big data platform—with the engine that designs and simulates the actual molecules for those targets—our physics-based AI drug platform. Completing both sides of this stack ahead of others gives us a distinct competitive edge,” he said.

SyntekaBio has invested about 50 billion won to construct a 15MW ABS Center on a 3,000-pyeong site in the Daedeok Innopolis(Daejeon science belt). The five-story standalone building houses more than 5000 CPU and GPU clusters. It is highly unusual for a company of SyntekaBio’s size to pour hundreds of billions of won-equivalent into building its own data center, and the ongoing operating costs are so substantial that, according to Jung, there are virtually no cases of domestic AI drug discovery companies—or even overseas players like Schrödinger—running their own data centers on a comparable scale.

Leveraging this next-generation AI drug development platform and data center, the company has recently signed a series of co-development deals for drug candidates with leading pharmaceutical and biotech companies in Korea and abroad. To date, SyntekaBio has supplied a total of 30 targets to its partners, including eight membrane proteins, five antibody targets, three immune-related targets and seven kinases.

“In the past, AI drug discovery platforms were essentially geared toward finding a single therapy against a single target,” Jung explained. “By contrast, SyntekaBio is the first in the world to combine an AI platform with a bio data center, enabling multi-target discovery for precision therapies such as oncology, metabolic diseases and cancer vaccines.”

He recalled that, “We previously pushed ahead with multi-target projects across antibodies, small molecules and cancer vaccines, but the business scope was simply too broad and our manpower far too limited, so we ran into a number of challenges. To compete head-on with the massive technology and capital base of the U.S. and Europe, you need a strong track record backed by ample assets and talent, and at the time that was difficult for us.”

“AI data center–based drug discovery is not about ‘betting’ everything on the 1% success probability of a single candidate,” Jung said. “It’s about exploring more than 100 candidates in parallel and securing a statistically robust probability of success. This is the kind of model Korean companies are best positioned to execute, and it is precisely why large domestic conglomerates and the government should pay attention to this field.”

He added, “With our next-generation AI drug development platform and data center now complete, we are beginning to see tangible results in multi-target and complex-modality drug programs,” and expressed confidence that “once validation data starts to accumulate in earnest early next year, we expect to see much more proactive engagement from major global clients.”