Viva Biotech participated in thebell Pharma & Bio Forum 2026, held on April 27, 2026, in Seoul, Korea. At the forum, Dr. Derek Ren, CEO of Viva Biotech (Shanghai) Ltd., and Dr. Yue Qian, VP and Head of MARS (Multi-Modal AI-Rooted Solutions), delivered a keynote presentation titled “AI Meets Biotech: Revolutionizing First-in-Class & Best-in-Class Drug Discovery.” The presentation highlighted Viva Biotech's approach to integrating artificial intelligence with structure-based drug discovery, with a focus on how AI-enabled modeling, experimental feedback, and closed-loop optimization can support the discovery of first-in-class and best-in-class therapeutics from target understanding to PCC generation.

Dr. Derek Ren, CEO of Viva Biotech (Shanghai) Ltd.

Dr. Yue Qian, VP and Head of MARS at Viva Biotech

At the core of Viva Biotech's AI-driven strategy is a structure-first understanding of molecular recognition. Building on its long-standing expertise in structure-based drug discovery, Viva Biotech examines how molecules interact with targets, from binding modes and conformational dynamics to structure–mechanism relationships. These structural and mechanistic insights support the development of mechanism-aware models with improved transferability and generalizability across targets and modalities. This structure-informed approach provides a foundation for exploring beyond known chemical space, particularly in programs with limited prior data or complex target biology.

Building on this structure-first foundation, MARS helps to shift drug discovery from screening-centered workflows toward generation-driven design. By integrating physics-based and data-driven models, MARS supports direct candidate generation, multi-parameter optimization, and rapid design-test-learn cycles. Through Viva Biotech’s lab-in-the-loop AI workflow, experimental data from protein production, structural biology, chemistry, biology, and DMPK evaluation are used to continuously refine models, while model outputs guide subsequent experimental priorities and molecular design. This closed-loop process enables continuous model improvement and more efficient discovery cycles.

Built on its long-standing expertise in structure-based drug discovery, Viva Biotech has developed an integrated discovery platform that connects AI-enabled modeling with experimental validation and translational research capabilities. To date, the company has studied more than 2,000 independent targets and solved close to 100,000 protein structures, providing a strong experimental and structural foundation for AI-driven discovery. Together, these capabilities point to a more precise, iterative, and experimentally grounded future for AI-driven drug discovery.