GlycoNex Adopts Advanced AI Computing to Accelerate Anti-Glycan Antibody Development

The company deploys high-performance AI computing and quantum-mechanics simulation to sharpen antibody specificity at the design stage and accelerate discovery.

NEW TAIPEI CITY, Taiwan — January 6, 2026 — GlycoNex, Inc. (TWO:4168) today announced that it has deployed a high-performance AI computing platform for quantum-mechanics simulation and antibody design, strengthening its capabilities for next-generation glycan-directed antibody-drug conjugate research. The system is used to evaluate the key interaction points between an antibody and its carbohydrate antigen and to optimize specificity, with the goal of designing antibodies with higher tumor selectivity.

GlycoNex has more than a decade of experience applying 3D structural simulation to antibody design and optimization, supported by an extensive antibody-development database. The newer AI computing and quantum-mechanics simulation extend this foundation — enabling analysis of antibody–antigen interactions at higher resolution and improving the efficiency of candidate screening.

By elevating computation from conventional structural modeling to the quantum-mechanics level, atomic-scale calculation can more precisely predict the electron distribution and binding energy between an antibody and a tumor-associated carbohydrate antigen. High-specificity candidates can therefore be selected at the design stage, reducing impact on healthy tissue and supporting drug safety.

All of the company's prior drug programs have incorporated computational simulation to complete antibody humanization and specificity optimization. Building on that hands-on experience and accumulated data, and with the cost of computing falling substantially, we expect higher-order quantum-mechanics simulation to further accelerate our drug development and provide more effective treatment options for cancer patients.

— Dr. Mei-Chun Yang, President and CEO, GlycoNex, Inc.

GlycoNex is also evaluating the use of AI in process development (CMC) — spanning process-parameter exploration, data integration, and critical-quality analysis — with the aim of shortening experimental timelines, improving process-development efficiency, and reducing manufacturing cost. Further detail on the company's technology is available on the Technology overview.

Contact

Media: GlycoNex, Inc. — mail@glyconex.com.tw (please indicate "Media Inquiry" in the subject line).

Issued by GlycoNex, Inc.

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