▷Speaker

Dr. Jaewoo Kang has been a professor in the Department of Computer Science and Engineering at Korea University since 2006. Dr. Kang also has served as the CEO of AIGEN Sciences Inc. from 2021. Before he joined Korea University, he taught students for three years from 2003 to 2006 as an assistant professor in the Department of Computer Science, North Carolina State University, USA. He received his PhD in Computer Science from the University of Wisconsin-Madison in 2003. From 2000 to 2001, he founded and operated WISEngine Inc., and from 1996 to 1997, worked at AT&T Labs Research in Florham Park, NJ.

He studies machine learning, natural language processing, and data mining, and applies them to solving problems with unmet needs in areas such as drug development and precision medicine.​ 

▷Lecture

The process of developing a new drug is long and difficult. In general, it takes more than 10 years of R&D period and 2.6 billion dollars of R&D costs before a single drug is approved. The bigger problem is that these costs are increasing every year. Some domain experts warn that if this trend continues, the pharmaceutical industry will become an industry with no ROI in a few years. In the pharmaceutical sector, there is a lot of interest in introducing AI to improve the chronic high cost and low efficiency structure of this industry. Can AI help solve this problem? AI has shown promise in many problems in the process of drug development, but I think there are still many areas lacking to show practical results. This talk outlines the drug development process from target identification, hit discovery, lead optimization, and clinical trial design, and discusses the contribution of AI at each stage and the challenges that current AI technology has to solve. 

▷Speaker

SunSik Kim is a country director at Intralinks with profound experiences in sales and business development at high-growth tech companies. He graduated from Yonsei University and earned MBA degree from ESMT Berlin in Germany. He is helping bio/pharma companies enable secure collaboration, achieve real-time transparency of clinical trials, and accelerate out-licensing activities. 

▷Lecture

AI and Big Data are expected to paly a major role in new drug discovery and development process. We will explore the newest technology trends in the industry  and address how companies are positioning their strategies and what are the business opportunities to be considered. 

- Global Trend in adopting AI and Big Data in new drug development

- The promise and challenges of new technologies for drug development​ 

▷Speaker

After receiving his PhD in medicinal chemistry from the UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill, Sung Jin Cho focused on developing computational and visualization tools to solve drug discovery-related problems while working at Bristol-Myers Squibb, Amgen, and CHDI.  He founded CIMPLRX, an explainable AI drug discovery company, in 2017 and is currently serving as the CEO. 

▷Lecture

Various big data are accumulated throughout the new drug development process, and efforts to optimize the R&D process and reduce the time and cost of new drug development continue. Most global pharmaceutical companies recognize the role of artificial intelligence in using big data and are actively applying AI to new drug development in various ways, and accordingly, the number of collaboration cases between pharmaceutical companies and AI companies is continuously increasing. Recently, there have been a number of new drug candidates developed by AI companies entering the clinical stage, and we would like to analyze these cases. 

▷Speaker

■ Education : Ph.D of Soongsil Univ. Computational Chemistry

■ ChongKunDang Pharmaceutical : Principle Scientist, Drug Design

■ CrystalGenomics, Principle Scientist, Drug Design 

■ BMDRC, Deputy Director​ 

▷Lecture

As the convergence of advanced digital technologies such as artificial intelligence and big data accelerates recently, 1. Uncertain possibility of success of new drug development 2. Long-term development 3. How to overcome large investment requirements, and present institutional improvements in Korea.