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The ultimate goal of chemistry is to design molecules with desired properties and synthesize them on demand. This can be achieved only by being able to freely manipulate matter at the atomic scale. Sounds unrealistic for now, but the dream can come true by continuing efforts toward the ultimate goal. To this end, we apply digital technology to chemistry. Traditional computational chemistry allows us to simulate chemical phenomena on a computer. These tools are based on physical principles such as classical mechanics, quantum mechanics, and statistical thermodynamics. However, they have been regarded as supplementary means of explaining experimental observations and validating intuitive ideas, because only human intelligence can design molecules and propose experiments for synthesis. Thus, intelligence is a key source of scientific creativity.
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Our group challenges integrating artificial intelligence into computational chemistry. AI is transforming industry, science, and even daily life. Chemistry is not an exception. AI as a powerful data-driven approach can be the best complementary of the physics-based approach. We have already developed deep learning models for smart molecular design, especially focused on drug discovery. We expand this approach to designing materials such as OLED. We are also developing digital tools to automatically predict reaction mechanisms and synthetic pathways. Moreover, cloud computing gives unlimited power to the computational approach. In such a way, modern digital technology is changing chemistry. This is just the beginning of intelligent chemistry.
Our group challenges integrating artificial intelligence into computational chemistry. AI is transforming industry, science, and even daily life. Chemistry is not an exception. AI as a powerful data-driven approach can be the best complementary of the physics-based approach. We have already developed deep learning models for smart molecular design, especially focused on drug discovery. We expand this approach to designing materials such as OLED. We are also developing digital tools to automatically predict reaction mechanisms and synthetic pathways. Moreover, cloud computing gives unlimited power to the computational approach. In such a way, modern digital technology is changing chemistry. This is just the beginning of intelligent chemistry.

News & Event

Seokhyun M., Joongwon L., and Seonghwan S. participated in Accelerate Conference 2023

지난 2023년 8월 21일부터 8월 25일까지 토론토에서 진행된 Accelerate Conference 2023에 문석현 학생, 서성환 학생, 이중원 학생이 참석하였다. Accelerate Conference 2023은 AI 기반 화학 분야의 학계, 산업계 인사들이 모여 교류하기 위해 올해 처음으로 개설된 학회로, 캐나다 토론토 대학에서 닷새 간 진행되었다. 우리 연구실은 Accelerate Conference 2023의 Material Topic에 포스터 발표로 참석하였다. 문석현 학생: Improving versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening 서성환 학생: Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly 이중원 학생: Robust Classification Framework with Unsupervised Learning
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Wonho Z., Seokhyun M., and Woo Youn K. participated in APATCC-10

지난 2023년 2월 19일부터 2월 23일까지 진행된 APATCC-10에 김우연 교수님을 포함한 정원호 학생, 문석현 학생이 참석하였다. APATCC-10은 아시아-태평양 지역에서 이론 및 계산화학을 다루는 학회로, 베트남의 ICISE에서 닷새 간 진행되었다. 우리 연구실은 APATCC-10의 총 10가지 세션 중 ML(Machine Learning) 세션에 참석하였다. 각각 아래의 주제로 김우연 교수님 및 정원호 학생은 Oral Presentation을, 문석현 학생은 Poster Presentation을 진행하였다. 김우연 교수님: Deep Learning-based Molecular Design with High Synthetic Feasibility 정원호 학생: A Target Protein-focused 3D Molecular Generative Framework toward Generalized Structure-based Drug Design 문석현 학생: Augmenting Deep Learning-Based Scoring Functions with Physically Motivated Data for Enhanced Drug-Target Interaction Prediction
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Hyeonsu K., Seonghwan K., and Junhyeong K. took 2nd-place in Samsung AI Challenge 2022!

지난 2022년 11월 08일 화요일, 우리 연구실 김성환 학생이 2022 Samsung AI Challenge 시상식에 2등의 성적으로 참석하였다. 2022 Samsung AI Challenge는 삼성 종합기술원(이하 SAIT)이 주최하고 DACON이 운영한 AI 챌린지로, 올해 8월 8일부터 9월 16일까지 진행되었다. 이번 대회는 두 가지 주제로 진행되었는데, 우리 연구실의 김현수, 김성환, 김준형 학생은 그 중 Materials Discovery 주제에 한 팀으로써 참여하였다. 해당 주제는 유기 반도체의 바닥 상태(ground state)와 들뜬 상태(excieted state) 사이에 발생하는 구조 및 물성 차이를 예측하는 AI 알고리즘 개발이 목적이었다. 우리 연구실 학생들은 해당 대회에 참여하여 2등의 성적을 달성하였고, 올해 진행된 AI Forum 2022의 시상식에 초대되었다.
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