Hyun+Uk+Kim
-
Czech Technology Mission with KAIST
Members of the Czech research community visited KAIST to discuss medium to long-term cooperation with KAIST. This visit was hosted by the Fourth Industrial Revolution Intelligence Center (FIRIC).
The community is comprised of people from Czech enterprises and academic institutes that are leading core technologies for the Fourth Industrial Revolution in the fields of AI, robotics, and biotechnology. They had a chance to meet KAIST professors and visit research labs.
Professor Il-Doo Kim from the Department of Materials Science and Engineering, Professor Seongsu Kim from the Department of Mechanical Engineering, and Professor Hyun Uk Kim from the Department of Chemical and Biomolecular Engineering attended the meeting, which took place in the Mechatronics, Systems, and Control Lab under the Vice President for Planning and Budget Soo Hyun Kim and Professor Kyung Soo Kim from the Department of Mechanical Engineering.
Professor Petr Novák from the Technical University of Ostrava said, “It was a meaningful meeting to help understand research trends on industrial robots in Korea.” Professor So Young Kim from FIRIC said, “The Czech research community is strong in basic research where KAIST has outstanding source technology. I hope this visit will open up a path for medium to long-term cooperation on sharing research and technology know-how between the Czech research community and KAIST.”
2018.06.07 View 5655 -
Cross-Generation Collaborative Labs Open
KAIST opened two cross-generation collaborative labs last month. This novel approach will pair up senior and junior faculty members for sustaining research and academic achievements even after the senior researcher retires.
This is one of the Vision 2031 innovation initiatives established to extend the spectrum of knowledge and research competitiveness. The selected labs will be funded for five years and the funding will be extended if necessary. KAIST will continue to select new labs every year.
A five-member selection committee including the Nobel Laureates Professor Klaus Von Klitzing at the Max-Planck Institute for Solid State Research and Dr. Kurt Wüthrich from ETH Zürich selected the first two labs with senior-junior pairs in March.
(Two renowned scholars' Cross-Generation Collaborative Labs which opened last month.
Distinguished Professor Lee's lab (above) andChair Professor Sung's lab)
Both labs are run by world-renowned scholars: the Systems Metabolic Engineering and Systems Healthcare Laboratory headed by Distinguished Professor Sang-Yup Lee in the Department of Chemical and Biomolecular Engineering and the Acousto-Microfluidics Research Center for Next-Generation Healthcare led by Chair Professor Hyung Jin Sung in the Department of Mechanical Engineering.
Distinguished Professor Lee will be teamed up with Professor Hyun Uk Kim, and their lab aims to mass produce new eco-friendly chemical materials as well as higher-value-added materials which will be used for medicine. The new platform technologies created in the lab are expected to provide information which will benefit human healthcare.
Meanwhile, the Acousto-Microfluidics Research Center for Next-Generation Healthcare will team up with Professors Hyoungsoo Kim and Yeunwoo Cho under Chair Professor Sung. The lab will conduct research on controlling fluids and objects exquisitely on a micro-nano scale by using high-frequency acoustic waves. The lab plans to develop a next-generation healthcare platform for customized diagnoses as well as disease treatment.
KAIST President Sung-Chul Shin, who introduced this novel idea in his research innovation initiative, said that he hopes the Cross-Generation Collaborative Labs will contribute to honoring senior scholars’ research legacies and passing knowledge down to junior researchers in order to further develop their academic achievements. He said, “I sincerely hope the labs will make numerous research breakthroughs in the very near future.”
2018.05.03 View 9910 -
Deep Learning Predicts Drug-Drug and Drug-Food Interactions
A Korean research team from KAIST developed a computational framework, DeepDDI, that accurately predicts and generates 86 types of drug-drug and drug-food interactions as outputs of human-readable sentences, which allows in-depth understanding of the drug-drug and drug-food interactions.
Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction.
To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled “Deep learning improves prediction of drug-drug and drug-food interactions.”
DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair. DeepDDI uses deep neural network to predict 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. Very importantly, DDI types predicted by DeepDDI are generated in the form of human-readable sentences as outputs, which describe changes in pharmacological effects and/or the risk of ADEs as a result of the interaction between two drugs in pair. For example, DeepDDI output sentences describing potential interactions between oxycodone (opioid pain medication) and atazanavir (antiretroviral medication) were generated as follows: “The metabolism of Oxycodone can be decreased when combined with Atazanavir”; and “The risk or severity of adverse effects can be increased when Oxycodone is combined with Atazanavir”. By doing this, DeepDDI can provide more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date.
DeepDDI was first used to predict DDI types of 2,329,561 drug pairs from all possible combinations of 2,159 approved drugs, from which DDI types of 487,632 drug pairs were newly predicted. Also, DeepDDI can be used to suggest which drug or food to avoid during medication in order to minimize the chance of adverse drug events or optimize the drug efficacy. To this end, DeepDDI was used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects to keep only the beneficial effects. Furthermore, DeepDDI was applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents were also finally predicted. All these prediction results can be useful if an individual is taking medications for a specific (chronic) disease such as hypertension or diabetes mellitus type 2.
Distinguished Professor Sang Yup Lee said, “We have developed a platform technology DeepDDI that will allow precision medicine in the era of Fourth Industrial Revolution. DeepDDI can serve to provide important information on drug prescription and dietary suggestions while taking certain drugs to maximize health benefits and ultimately help maintain a healthy life in this aging society.”
Figure 1. Overall scheme of Deep DDDI and prediction of food constituents that reduce the in vivo concentration of approved drugs
2018.04.18 View 10354