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The research team led by Prof. Jaewoo Kang wins Multi-targeting ...
  • 글쓴이 : Communications Team
  • 조회 : 484
  • 일 자 : 2018-12-26


The research team led by Prof. Jaewoo Kang wins Multi-targeting Drug DREAM Challenge.

Prof. Kang’s team also ranked highest NCI-CPTAC DREAM Proteogenomics Computational Challenge last year, the first as a Korean team in DREAM Challenges.

Prof. Kang’s team, mainly consisting of AI majors, outperformed global pharmaceutical companies.

They plan to work together with a research team from Mount Sinai School of Medicine.


 

(From left) Hwisang Jeon, master’s degree student; Jinhyeok Lee, integrated master’s-doctoral degree student; Avner Schlessinger, Professor in the Department of Pharmacology and Systems Therapeutics at Mount Sinai; Ross Cagan, Professor at Mount Sinai School of Medicine; Minji Jeon, Research Professor; Jaewoo Kang, Professor; Donghyeon Park, doctoral degree student; and Miyoung Ko, master’s degree student



The research team led by Prof. Jaewoo Kang of the Department of Computer Science and Engineering, College of Informatics, won first place in the Multi-targeting Drug DREAM Challenge.

 

Prof. Kang’s team, mainly consisting of AI specialists majoring in computer science and with little knowledge of new drug development (Minji Jeon, Research Professor; Donghyeon Park, doctoral degree student; Jinhyeok Lee, integrated master-doctoral degree student; Hwisang Jeon, master’s degree student; Miyoung Ko, master’s degree student; and Aik-Choon Tan, Professor at University of Colorado School of Medicine), outperformed multinational pharmaceutical firms such as Janssen and Bayer in the Challenge that that took place in New York, US, on December 8.

 

In 2016, the team took second place in the Drug Combination Prediction DREAM Challenge, hosted by AstraZeneca in partnership with Wellcome Trust Sanger Institute. In 2017, they also ranked highest in the NCI-CPTAC DREAM Proteogenomics Computational Challenge, a global precision medicine competition for predicting the characteristics of tumor proteomes. This was the first time for a Korean team to place as best performer in the DREAM Challenge.

 

The award ceremony was held during the 2018 RECOMB/ISCB Conference on Regulatory and Systems Genomics with Dream Challenges in New York, US, on December 8 (local time).

 

DREAM Challenges, which started in 2007 as hosted by IBM Research and Sage Bionetworks, are renowned global challenges in biomedicine. Throughout the 49 challenges so far, over 10,000 scientists from prestigious universities, including Harvard, MIT and Stanford, and health care institutions such as MD Anderson Cancer Center and Sloan Kettering Cancer Center, and AstraZeneca and other pharmaceutical firms have participated to collaborate and compete to solve challenges in precision medicine.

 

During the DREAM Challenge this year hosted by Mount Sinai School of Medicine, teams had to solve two questions on discovering candidate substances for new drugs to treat medullary thyroid cancer and tauopathy.

 

Prof. Kang’s team utilized gene expression reactions caused by drugs and developed an algorithm based on deep learning technology, thus selecting candidate substances for new drugs out of 230 million compounds. The host institution tested and verified the substances Prof. Kang’s team chose to confirm the potential for developing them into new drugs.

 

The victory of Prof. Kang’s team is more meaningful, in that the team, mainly composed of AI specialists with little knowledge of new drug development, outperformed global companies in drug discovery such as Janssen, Bayer and Immuneering.

 

The deep learning algorithm developed by Prof. Kang’s team suggested a new method to drastically improve the delivery process of candidate substances for new drugs. Today, the process heavily depends on the structure of drugs and takes 10-15 years on average to develop a new drug. However, the success rate of new drug development stands at a mere 0.02%. Drug development costs, 2.5 billion dollars on average, are mostly incurred by high failure rates in drug delivery. The research outcomes of Prof. Kang’s team are expected to remove inefficiency and cut costs in the process of discovering candidate materials for new drugs by using AI technology.

 

Prof. Kang’s team will work together with a research team led by Prof. Ross Cagan from Mount Sinai School of Medicine, located in New York, to verify the drug discovery pipeline and identify candidate compounds for new drugs that can be advanced to the clinical stage.

 

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