Jaewoo Kang
Founder & Chief Executive Officer AIGEN Sciences
Dr. Jaewoo Kang is Founder and CEO of AIGEN Sciences, an AI-driven drug discovery company focused on accelerating the development of innovative therapeutics through the integration of artificial intelligence and human expertise. A Professor of Computer Science at Korea University, he is widely recognized for developing BioBERT, one of the most influential biomedical AI language models, and for his contributions to computational biology, bioinformatics, and drug discovery. Under his leadership, AIGEN Sciences has advanced a growing pipeline of small-molecule and ADC programs while establishing partnerships that showcase the potential of AI-enabled pharmaceutical innovation.
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
- Introducing a self-driving drug discovery framework in which LLM-based agents orchestrate domain-specific foundation models to support target prioritization, hypothesis generation, and molecular design
- Highlighting applications to integrate target identification, ternary complex structure prediction, interface-aware molecular design, and degradation activity prediction to efficiently identify productive molecular glues
- Exploring how ID-free DIA-MS proteomics data is incorporated as a multi-modal biological input to support biomarker discovery, enabling more robust target prioritization and validation within the autonomous AI framework
