Syntekabio

Syntekabio is a South Korean-based drug discovery and development company that leverages artificial intelligence (AI) and its own supercomputing infrastructure to accelerate the identification of novel therapies. Founded in 2009 by Jongsun Jung, Ph.D., a biochemical computational scientist with a background at the U.S. and Korea National Institutes of Health, the company has established itself in the biotech sector by integrating genomic big data with proprietary deep-learning algorithms. Syntekabio became the first AI drug discovery company to be listed on South Korea's KOSDAQ exchange in December 2019.

The company's business model centers on providing a suite of AI-driven platforms as a service and engaging in joint research and development partnerships with pharmaceutical companies, biotech firms, and research institutions. Revenue is generated through these collaborations, which can involve upfront payments, milestone achievements, and future royalties on co-developed drugs. Syntekabio serves a global clientele, including entities in the U.S. and Korea, and has established a U.S. subsidiary in New York City to facilitate international partnerships. The firm actively collaborates with partners such as Enamine for compound libraries, Pragma Biosciences for microbiome-based therapies, and Proteco for COVID-19 treatments.

At the core of its operations is the STB CLOUD, an automated, one-stop AI drug discovery platform. This service integrates the company's key technologies, including DeepMatcher®, a platform for identifying and optimizing small molecule drug candidates by predicting how they will bind to target proteins. The platform is also expanding to include NEO-ARS™ for predicting neoantigens for cancer vaccines, and Ab-ARS™ for antibody drug discovery. Supporting these platforms is the company's substantial investment in physical infrastructure, notably the AI Bio-Supercom Center in Daejeon. This facility houses thousands of servers, providing the massive computing power necessary to screen billions of compounds and conduct complex molecular dynamics simulations, significantly reducing the time and cost associated with early-stage drug discovery.

Keywords: AI drug discovery, supercomputing, genomic big data, KOSDAQ, DeepMatcher, STB CLOUD, computational biology, drug development, preclinical candidate, small molecule discovery, lead optimization, neoantigen prediction, antibody discovery, personalized medicine, biomarker discovery, joint drug development, pharmaceutical partnerships, biotech collaboration, protein-ligand interaction, molecular dynamics simulation