Namshik Han

Namshik Han is a computational drug discovery scientist currently serving as Head of Computational Research & AI at the Milner Therapeutics Institute and Associate Faculty at the Cambridge Centre for AI in Medicine, both within the University of Cambridge. He also holds an Adjunct Professor position at Yonsei University College of Medicine. His work bridges academic research with industry applications through co-founding two startups: KURE.ai Therapeutics in the USA (focused on immune-oncology NK cell therapy) and CardiaTec Biosciences in the UK (dedicated to cardiovascular disease drugs), as well as contributing to the establishment of Storm Therapeutics.

Dr. Han's research centers on developing and applying specialized Artificial Intelligence technologies to analyze complex multi-modal biomedical datasets. His lab at the Milner Therapeutics Institute focuses on enhancing AI technology for therapeutics research through machine learning, statistical analysis, and mathematical techniques applied to multi-omics and drug discovery. Key research areas include identifying novel therapeutic targets across diseases, drug repositioning, predicting drug efficacy and safety, and patient stratification for personalized medicine. His work leverages both publicly available big data and purposefully generated experimental data from academic collaborators and pharma/biotech partners.

Analysis of Dr. Han's recent publications reveals a strong trend toward applying AI methodologies across diverse biomedical challenges. His work spans RNA methylation pathway prediction, drug-induced liver injury detection through NLP, cancer immunotherapy target identification using CRISPR screens, and SARS-CoV-2 pathway analysis for drug repurposing. These publications demonstrate his expertise in translating computational approaches to address specific therapeutic challenges across multiple disease areas.

Dr. Han actively facilitates access to state-of-the-art AI technology for partner organizations within the Milner Consortium while developing novel computational methods. His entrepreneurial activities through startup companies indicate significant engagement with translational research and commercialization of academic discoveries. His laboratory utilizes interdisciplinary approaches combining computational methods with experimental validation across therapeutic areas.

The lab has developed innovative approaches such as utilizing Artificial Neural Networks to predict the Mode of Action of drugs by simulating drugs on protein-protein interaction networks, as published in Science Advances (2021). This work exemplifies their strategy of bridging virtual simulations with real-world biological validation to advance therapeutic discovery.