DSpace at KOASAS: Browsing DSpace

DSpace at KOASAS

Browsing DSpace

Browse "Dept. of Chemistry(화학과)" by Author Lim, Jaechang

Sort by:
In order:
Results/Page
Authors/Record:

Showing results 1 to 22 of 22

1	Configuration interaction singles and doubles using Kohn-Sham orbitals with local exchange potential Lim, Jaechang; Choi, Sunghwan; Kim, Jaewook; Kim, Woo Youn, 4th Conference on Theory and Applications of Computational Chemistry, University of Washington, 2016-08-30
2	Configurational Interaction method using Kohn-Sham Orbitals for the real-space numerical grid based DFT program Kim, Jaewook; Lim, Jaechang; Hong, Kwang Woo; Choi, Sunghwan; Hwang, Sang Yeon; Kim, Woo Youn, The seventh asia-pacific conference of theoretical and computational chemistry, National Tsing Hua University, 2016-01-27
3	Development of deep learning methods for efficient early stage drug discovery = 효과적인 초기 신약후보물질 발굴을 위한 딥러닝 방법론 개발 link Lim, Jaechang; Kim, Woo Youn; et al, 한국과학기술원, 2020
4	DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening Kim, Hyeongwoo; Lee, Kyunghoon; Kim, Chansu; Lim, Jaechang; Kim, Woo Youn, JOURNAL OF CHEMICAL INFORMATION AND MODELING, v.64, no.7, pp.2432 - 2444, 2023-08
5	Drug-likeness scoring based on unsupervised learning Lee, Kyunghoon; Jang, Jinho; Seo, Seonghwan; Lim, Jaechang; Kim, Woo Youn, CHEMICAL SCIENCE, v.13, no.2, pp.554 - 565, 2022-01
6	Homochiral Supramolecular Thin Film from Self-Assembly of Achiral Triarylamine Molecules by Circularly Polarized Light Park, Changjun; Lee, Jinhee; Kim, Taehyoung; Lim, Jaechang; Park, Jeyoung; Kim, Woo Youn; Kim, Sang Youl, MOLECULES, v.25, no.2, 2020-01
7	Improvement of initial guess via grid-cutting for efficient grid-based density functional calculations Lim, Jaechang; Choi, Sunghwan; Kang, Sungwoo; Kim, Jaewook; Hong, Kwangwoo; Kim, Woo Youn, INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, v.116, no.19, pp.1397 - 1403, 2016-10
8	Kohn-Sham approach for fast hybrid density functional calculations in real-space numerical grid methods Kim, Jaewook; Kang, Sungwoo; Lim, Jaechang; Hwang, Sang-Yeon; Kim, Woo Youn, COMPUTER PHYSICS COMMUNICATIONS, v.230, pp.21 - 26, 2018-09
9	Molecular Generative Model Based on an Adversarially Regularized Autoencoder Hong, Seung Hwan; Ryu, Seongok; Lim, Jaechang; Kim, Woo Youn, JOURNAL OF CHEMICAL INFORMATION AND MODELING, v.60, no.1, pp.29 - 36, 2020-01
10	Molecular generative model based on conditional variational autoencoder for de novo molecular design Lim, Jaechang; Ryu, Seongok; Kim, Jin Woo; Kim, Woo Youn, JOURNAL OF CHEMINFORMATICS, v.10, 2018-07
11	Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly Seo, Seonghwan; Lim, Jaechang; Kim, Woo Youn, ADVANCED SCIENCE, v.10, no.8, 2023-03
12	New way of making initial guess for Iterative Self Consistent Field Procedure in Grid based method Lim, Jaechang; Choi, Sunghwan; Kang, Sungwoo; Hong, Kwang-Woo; Kim, Woo-Youn, IUPAC-2015, Korean Chemical Society, 2015-08-13
13	Non-empirical atomistic dipole-interaction-model for quantum plasmon simulation of nanoparticles Lim, Jaechang; Kang, Sungwoo; Kim, Jaewook; Kim, Woo Youn; Ryu, Seol, SCIENTIFIC REPORTS, v.7, 2017-11
14	Outstanding performance of configuration interaction singles and doubles using exact exchange Kohn-Sham orbitals in real-space numerical grid method Lim, Jaechang; Choi, Sunghwan; Kim, Jaewook; Kim, Woo Youn, JOURNAL OF CHEMICAL PHYSICS, v.145, no.22, 2016-12
15	PIGNet2: a versatile deep learning-based protein-ligand interaction prediction model for binding affinity scoring and virtual screening Moon, Seokhyun; Hwang, Sang-Yeon; Lim, Jaechang; Kim, Woo Youn, DIGITAL DISCOVERY, v.3, no.2, pp.287 - 299, 2024-02
16	PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions Moon, Seokhyun; Zhung, Wonho; Yang, Soojung; Lim, Jaechang; Kim, Woo Youn, CHEMICAL SCIENCE, v.13, no.13, pp.3661 - 3673, 2022-04
17	Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks Lim, Jaechang; Ryu, Seongok; Park, Kyubyong; Choe, Yo Joong; Ham, Jiyeon; Kim, Woo Youn, The 5 th International Conference on Molecular Simulation, The Korean Institute of Metals and Materials, The Korea Institute of Science and Technology, Korea Advanced Institute of Science and Technology - ACE Team, Seoul National University, 2019-11-05
18	Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation Lim, Jaechang; Ryu, Seongok; Park, Kyubyong; Choe, Yo Joong; Ham, Jiyeon; Kim, Woo Youn, JOURNAL OF CHEMICAL INFORMATION AND MODELING, v.59, no.9, pp.3981 - 3988, 2019-09
19	Scaffold-based molecular design using a graph generative model Lim, Jaechang; Hwang, Sang-Yeon; Moon, Seokhyun; Kim, Seungsu; Kim, Woo Youn, 10th Triennial Congress of the International Society for Theoretical Chemical Physics, UiT The Arctic University of Norway, 2019-07-15
20	Scaffold-based Molecular Design Using Graph Generative Model Lim, Jaechang; Hwang, Sang-Yeon; Kim, Seungsu; Moon, Seokhyun; Kim, Woo Youn, The 2019 Asia-Pacific Association of Theoretical and Computational Chemists, APATCC 2019, 2019-09-30
21	Scaffold-based molecular design with a graph generative model Lim, Jaechang; Hwang, Sang-Yeon; Moon, Seokhyun; Kim, Seungsu; Kim, Woo Youn, CHEMICAL SCIENCE, v.11, no.4, pp.1153 - 1164, 2020-01
22	Study of Li Adsorption on Graphdiyne Using Hybrid DFT Calculations Kim, Jaewook; Kang, Sungwoo; Lim, Jaechang; Kim, Woo Youn, ACS APPLIED MATERIALS & INTERFACES, v.11, no.3, pp.2677 - 2683, 2019-01

Discover

KOASAS

Knowledge Service Development Team, KAIST 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea. T. 82-42-350-4493 Email. koasas@kaist.ac.kr
Copyright © 2016. Korea Advanced Institute of Science and Technology. All Rights Reserved.

Communities & Collections

rss_1.0 rss_2.0 atom_1.0