Search research articles
Contact Us
Filters
Showing results (1-10 of 10) with videos related to
Page
of 1
Sort By:
Chemical Science
|
December 6, 2019
A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
The Journal of Chemical Physics
|
March 10, 2016
Supersampling method for efficient grid-based electronic structure calculations
Seongok Ryu, Sunghwan Choi, Kwangwoo Hong, et al.
Journal of Cheminformatics
|
December 5, 2024
CSearch: chemical space search via virtual synthesis and global optimization
Hakjean Kim, Seongok Ryu, Nuri Jung, et al.
Journal of Chemical Information and Modeling
|
December 11, 2019
Molecular Generative Model Based on an Adversarially Regularized Autoencoder
Seung Hwan Hong, Seongok Ryu, Jaechang Lim, et al.
Journal of Cheminformatics
|
July 12, 2018
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, et al.
Journal of Chemical Information and Modeling
|
October 24, 2025
SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization
Jeonghyeon Kim, Seongok Ryu, Woohyeong Lee, et al.
Journal of Chemical Theory and Computation
|
August 7, 2024
GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy
Changsoo Lee, Jonghun Won, Seongok Ryu, et al.
Journal of Chemical Information and Modeling
|
August 25, 2019
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
Jaechang Lim, Seongok Ryu, Kyubyong Park, et al.
Physical Chemistry Chemical Physics : PCCP
|
April 5, 2017
Effects of the locality of a potential derived from hybrid density functionals on Kohn-Sham orbitals and excited states
Jaewook Kim, Kwangwoo Hong, Sang-Yeon Hwang, et al.
Journal of Chemical Information and Modeling
|
November 9, 2020
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks
Doyeong Hwang, Soojung Yang, Yongchan Kwon, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Chemical Science
|
December 6, 2019
A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
The Journal of Chemical Physics
|
March 10, 2016
Supersampling method for efficient grid-based electronic structure calculations
Seongok Ryu, Sunghwan Choi, Kwangwoo Hong, et al.
Journal of Cheminformatics
|
December 5, 2024
CSearch: chemical space search via virtual synthesis and global optimization
Hakjean Kim, Seongok Ryu, Nuri Jung, et al.
Journal of Chemical Information and Modeling
|
December 11, 2019
Molecular Generative Model Based on an Adversarially Regularized Autoencoder
Seung Hwan Hong, Seongok Ryu, Jaechang Lim, et al.
Journal of Cheminformatics
|
July 12, 2018
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, et al.
Journal of Chemical Information and Modeling
|
October 24, 2025
SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization
Jeonghyeon Kim, Seongok Ryu, Woohyeong Lee, et al.
Journal of Chemical Theory and Computation
|
August 7, 2024
GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy
Changsoo Lee, Jonghun Won, Seongok Ryu, et al.
Journal of Chemical Information and Modeling
|
August 25, 2019
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation
Jaechang Lim, Seongok Ryu, Kyubyong Park, et al.
Physical Chemistry Chemical Physics : PCCP
|
April 5, 2017
Effects of the locality of a potential derived from hybrid density functionals on Kohn-Sham orbitals and excited states
Jaewook Kim, Kwangwoo Hong, Sang-Yeon Hwang, et al.
Journal of Chemical Information and Modeling
|
November 9, 2020
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks
Doyeong Hwang, Soojung Yang, Yongchan Kwon, et al.
Page
of 1