• Bottlenecks in AI-Driven New Drug Development — Real-World Case Studies in Molecular Modeling and Talent Development Strategies 2026.08.26 (10:30 - 11:00) | The Platz Seminar Room AICADD KIM Young-hoon, Founder Detail View
  • New Era: Way to Autonomous Scientific Discovery — Empowered by AI & Robotics Infrastructure 2026.08.26 (11:00 - 11:30) | The Platz Seminar Room XtalPi Jae-Sung Hwang, Director of Business Development Detail View
  • P-20

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    Title New Era: Way to Autonomous Scientific Discovery — Empowered by AI & Robotics Infrastructure
    Date 2026.08.26 (11:00 - 11:30) | The Platz Seminar Room
    Organization XtalPi
    Speaker Jae-Sung Hwang, Director of Business Development
    Speaker & Lecture Information

    [Brief Introduction of Seminar]

    Drug discovery is a representative high-risk, high-cost field, requiring long development periods and substantial investment. With the recent breakthroughs in artificial intelligence, efforts are being made to alleviate bottlenecks in the drug development process by applying AI. However, the core bottleneck in drug discovery lies not only in the performance of algorithms themselves, but in establishing an “experimental virtuous cycle” — rapidly validating AI-predicted results through experiments and feeding the measured data back into the AI models.

     

    XtalPi’s autonomous laboratory is at the forefront of this paradigm shift. By integrating AI-based molecular design, predictive modeling, and automated experimental platforms, it aims to transform traditional research processes into an autonomous, closed-loop discovery system. Examining such systems reveals how they enable rapid iteration between hypothesis generation, experimental validation, and data-driven learning, thereby enhancing efficiency, reproducibility, and scalability in pharmaceutical and chemical research. 

     

    [Brief Introduction of Speaker]

    With 24 years of research experience in the pharmaceutical and biotech industry at companies such as JW Pharmaceutical and Dong-A ST, I have witnessed the challenges of drug discovery through numerous cases of failure. I hold a successful case of overseas licensing-out to AbbVie in 2016, and since 2023, I have been working at the AI Drug Discovery Convergence Research Center of the Korea Pharmaceutical and Bio-Pharma Manufacturers Association, conducting policy research on AI-driven drug discovery and proposing the adoption of AI drug development strategies to pharmaceutical companies. As one of the long-standing experts in computer-aided drug discovery in Korea, I am recognized as a Digital Molecular Designer. 

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