DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Woo Youn | - |
dc.contributor.advisor | 김우연 | - |
dc.contributor.author | Lim, Jaechang | - |
dc.date.accessioned | 2021-05-12T19:47:31Z | - |
dc.date.available | 2021-05-12T19:47:31Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947938&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284559 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 화학과, 2020.2,[iv, 69 p. :] | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep learning▼adrug discovery▼adrug-target interaction▼amolecular generative model▼ahit to lead | - |
dc.subject | 딥러닝▼a신약개발▼a분자-타겟 상호작용▼a분자생성모델▼ahit to lead | - |
dc.title | Development of deep learning methods for efficient early stage drug discovery | - |
dc.title.alternative | 효과적인 초기 신약후보물질 발굴을 위한 딥러닝 방법론 개발 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :화학과, | - |
dc.contributor.alternativeauthor | 임재창 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.