Hyun Mog Kim
Research Associate Korea University
Hyun Mog Kim is an AI researcher specialising in applying machine learning, mathematics, and physics to drug discovery. Driven by curiosity and first-principles thinking, he develops AI solutions for understanding and designing complex biomolecular systems. During his time at Arontier, he engineered AI-driven antibody design platforms that bridge computational modeling with experimental validation. He is passionate about combining scientific principles with practical AI systems to accelerate the discovery of next-generation therapeutics.
Seminars
AI has truly become the buzzword as the tool to improve the quality of, and accelerate, drug discovery timelines. But how impactful are AI/ML discovery tools, and what has been proven experimentally so far to demonstrate reliability?
Join this workshop to break down the applications and bottlenecks of AI/ML for de novo discovery, and learn how best to blend in silico and wet lab expertise.
Workshop Highlights Include:
- Seeing through the AI hype: Breaking down different augmentations of AI/ML tools to improve the quality and reduce cost and timelines of drug discovery workflows
- Understanding what has been shown to be effective so far through experimental validation, and what areas remain challenging – what are the core hesitancies to adopt AI/ML discovery tools?
- Limits and and failures of structure prediction tools: Understanding methods to steer AI/ML models to generate transition-state conformations without further training
- Integrating wet lab and computational insights to unlock mutual benefit towards best-in-class drugs
