Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hyeon-Nae Jeon"'
Autor:
Hocheol Lim, Doo Hyung Kang, Jeonghoon Kim, Aidan Pellow-Jarman, Shane McFarthing, Rowan Pellow-Jarman, Hyeon-Nae Jeon, Byungdu Oh, June-Koo Kevin Rhee, Kyoung Tai No
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQ
Externí odkaz:
https://doaj.org/article/dfd166ae7f7e4143acc2967d81ac3730
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)
Abstract Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglem
Externí odkaz:
https://doaj.org/article/07a015d08a2e4a00b36b94289f19d22e
Autor:
Hocheol Lim, Hyeon-Nae Jeon, Seungcheol Lim, Yuil Jang, Taehee Kim, Hyein Cho, Jae-Gu Pan, Kyoung Tai No
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 788-798 (2022)
The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequ
Externí odkaz:
https://doaj.org/article/46e43217550141708c48c32b971be256
Autor:
Jiashun Mao, Amir Zeb, Min Sung Kim, Hyeon-Nae Jeon, Jianmin Wang, Shenghui Guan, Kyoung Tai NO
Publikováno v:
Heliyon, Vol 8, Iss 8, Pp e10011- (2022)
Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles i
Externí odkaz:
https://doaj.org/article/b32d6d4f164b45e887a3ded2fb5e9cbf
Publikováno v:
ACS Omega, Vol 6, Iss 23, Pp 15361-15373 (2021)
Externí odkaz:
https://doaj.org/article/32b8d6968ac54907b533b4def88b088b
Autor:
Jiashun Mao, Javed Akhtar, Xiao Zhang, Liang Sun, Shenghui Guan, Xinyu Li, Guangming Chen, Jiaxin Liu, Hyeon-Nae Jeon, Min Sung Kim, Kyoung Tai No, Guanyu Wang
Publikováno v:
iScience, Vol 24, Iss 9, Pp 103052- (2021)
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The
Externí odkaz:
https://doaj.org/article/bb28c7b4621a49fa8a2942d4e2d7625e
Publikováno v:
ACS Omega
ACS Omega, Vol 6, Iss 23, Pp 15361-15373 (2021)
ACS Omega, Vol 6, Iss 23, Pp 15361-15373 (2021)
The objective of this study was to develop a robust prediction model for the infinite dilution activity coefficients (γ∞) of organic molecules in diverse ionic liquid (IL) solvents. Electrostatic, hydrogen bond, polarizability, molecular structure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1a69890fadf347a0aa3eee1ce49780f
http://europepmc.org/articles/PMC8210453
http://europepmc.org/articles/PMC8210453
Autor:
Jianmin Wang, Yanyi Chu, Jiashun Mao, Hyeon-Nae Jeon, Haiyan Jin, Amir Zeb, Yuil Jang, Kwang-Hwi Cho, Tao Song, Kyoung Tai No
Publikováno v:
Briefings in bioinformatics. 23(4)
We construct a protein–protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c45e527c4c1ea45bb64f72e29a808b5
https://pubmed.ncbi.nlm.nih.gov/35830870
https://pubmed.ncbi.nlm.nih.gov/35830870
Autor:
Hocheol Lim, Hyeon-Nae Jeon, Seungcheol Lim, Yuil Jang, Taehee Kim, Hyein Cho, Jae-Gu Pan, Kyoung Tai No
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 20, Iss, Pp 788-798 (2022)
The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd466c850843ffd833775284da7a9add
https://pubmed.ncbi.nlm.nih.gov/35222841
https://pubmed.ncbi.nlm.nih.gov/35222841
Autor:
Xiao Zhang, Hyeon-Nae Jeon, Jiaxin Liu, Guangming Chen, Shenghui Guan, Kyoung Tai No, Liang Sun, Javed Akhtar, Jiashun Mao, Xinyu Li, Guanyu Wang, Min Sung Kim
Publikováno v:
iScience, Vol 24, Iss 9, Pp 103052-(2021)
iScience
iScience
Summary Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f52cb8299cccfebc2b1ed14090829e5
https://doi.org/10.1016/j.isci.2021.103052
https://doi.org/10.1016/j.isci.2021.103052