Development of deep learning methods for efficient early stage drug discovery = 효과적인 초기 신약후보물질 발굴을 위한 딥러닝 방법론 개발

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Deep learning techniques attract great attention as a new promising way of reducing time and resources for finding new drug candidates. We address how deep learning techniques can accelerate the process of drug discovery: from hit discovery to lead optimization. In particular, our work focuses on developing deep learning techniques for an accurate prediction of drug-target interaction (DTI) and molecular generative models for designing molecules with desirable molecular properties. As a result, we significantly improved the accuracy of DTI prediction compared to docking and other deep learning techniques. Also, we developed a series of molecular generative models with progressive technical advancement compared to previous models. We believe that our contribution can be the beginning of a long journey toward AI-based drug design.
Advisors
Kim, Woo Younresearcher김우연researcher
Description
한국과학기술원 :화학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 화학과, 2020.2,[iv, 69 p. :]

Keywords

Deep learning▼adrug discovery▼adrug-target interaction▼amolecular generative model▼ahit to lead; 딥러닝▼a신약개발▼a분자-타겟 상호작용▼a분자생성모델▼ahit to lead

URI
http://hdl.handle.net/10203/284559
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947938&flag=dissertation
Appears in Collection
CH-Theses_Ph.D.(박사논문)
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