Seongok Ryu

According to our database, Seongok Ryu authored at least 13 papers between 2018 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning.

Seongok Ryu, Sumin Lee

CoRR, 2022

2021

Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation.

Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.

Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung-Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu

J. Chem. Inf. Model., 2020

Molecular Generative Model Based on an Adversarially Regularized Autoencoder.

Seung Hwan Hong, Seongok Ryu, Jaechang Lim, Woo Youn Kim

J. Chem. Inf. Model., 2020

A benchmark study on reliable molecular supervised learning via Bayesian learning.

Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon, Seongok Ryu

CoRR, 2020

A comprehensive study on the prediction reliability of graph neural networks for virtual screening.

Soojung Yang, Kyung-Hoon Lee, Seongok Ryu

CoRR, 2020

2019

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim

J. Chem. Inf. Model., 2019

Molecular Generative Model Based On Adversarially Regularized Autoencoder.

Seung Hwan Hong, Jaechang Lim, Seongok Ryu, Woo Youn Kim

CoRR, 2019

Uncertainty quantification of molecular property prediction using Bayesian neural network models.

Seongok Ryu, Yongchan Kwon, Woo Youn Kim

CoRR, 2019

Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks.

Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim

CoRR, 2019

Uncertainty quantification of molecular property prediction with Bayesian neural networks.

Seongok Ryu, Yongchan Kwon, Woo Youn Kim

CoRR, 2019

2018

Molecular generative model based on conditional variational autoencoder for de novo molecular design.

Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim

J. Cheminformatics, 2018

Deeply learning molecular structure-property relationships using graph attention neural network.

Seongok Ryu, Jaechang Lim, Woo Youn Kim

CoRR, 2018