[Biography]
2021.08 – 현재 : Chief Executive Officer, Deargen USA, Atlanta, GA.
2020.05 – 2021.08 : Chief AI Officer/Co-founder, Deargen, Seoul, South Korea.
2019.08 – 2020.05 : Advisor, Deargen, Seoul, South Korea.
2015.08 – 2020.05 : Research Assistant, Atlanta, GA, Emory University.
2016 – 2018 : Teaching Assistant, Atlanta, GA, Emory University.
2016 summer – 2018 summer : Research Intern, Deargen, Seoul, South Korea.
2017 summer : Research Intern, VISA Research, Palo Alto, CA.

[Abstract]
Proposing a new drug candidate is an essential part of the drug discovery process, consisting of many sub-tasks. Traditionally, these tasks have been tackled by chemistry and pharmaceutical experts and take years to design. Therefore, many deep-learning models have been proposed to effectively accelerate drug discovery tasks.
In this seminar, how deep learning methods can be applied to each of the subtasks in the drug discovery process. Firstly, as a target identification tool, a new feature selection method for disease-related features will be introduced. Next, we will review a new drug-target interaction model based on Transformer architecture to overcome the limitation of the existing models. Lastly, a novel drug generation model that can modify an existing drug to best reflect the desired properties will be presented.
After reviewing these models, the limitations of these approaches will be highlighted so that we can properly guess the future research direction.

ZOOM : https://zoom.us/j/8978217407?pwd=d1pOZmF1OWlseEdZRVBpV3VuSkl3dz09