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Seongok Ryu
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2020 – today
- 2024
- [i11]Jeonghyeon Kim, Juno Nam, Seongok Ryu:
Understanding active learning of molecular docking and its applications. CoRR abs/2406.12919 (2024) - 2022
- [i10]Seongok Ryu, Sumin Lee:
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning. CoRR abs/2210.07145 (2022) - 2021
- [c1]Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang:
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation. NeurIPS 2021: 7924-7936 - [i9]Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, Sung Ju Hwang:
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation. CoRR abs/2110.01219 (2021) - 2020
- [j4]Seung Hwan Hong
, Seongok Ryu, Jaechang Lim
Molecular Generative Model Based on an Adversarially Regularized Autoencoder. J. Chem. Inf. Model. 60(1): 29-36 (2020) - [j3]Doyeong Hwang, Soojung Yang
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks. J. Chem. Inf. Model. 60(12): 5936-5945 (2020) - [i8]Soojung Yang, Kyung-Hoon Lee, Seongok Ryu:
A comprehensive study on the prediction reliability of graph neural networks for virtual screening. CoRR abs/2003.07611 (2020) - [i7]Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon, Seongok Ryu:
A benchmark study on reliable molecular supervised learning via Bayesian learning. CoRR abs/2006.07021 (2020)
2010 – 2019
- 2019
- [j2]Jaechang Lim
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. J. Chem. Inf. Model. 59(9): 3981-3988 (2019) - [i6]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction with Bayesian neural networks. CoRR abs/1903.08375 (2019) - [i5]Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim:
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks. CoRR abs/1904.08144 (2019) - [i4]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction using Bayesian neural network models. CoRR abs/1905.06945 (2019) - [i3]Seung Hwan Hong, Jaechang Lim, Seongok Ryu, Woo Youn Kim:
Molecular Generative Model Based On Adversarially Regularized Autoencoder. CoRR abs/1912.05617 (2019) - 2018
- [j1]Jaechang Lim
Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminformatics 10(1): 31:1-31:9 (2018) - [i2]Seongok Ryu, Jaechang Lim, Woo Youn Kim:
Deeply learning molecular structure-property relationships using graph attention neural network. CoRR abs/1805.10988 (2018) - [i1]Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim:
Molecular generative model based on conditional variational autoencoder for de novo molecular design. CoRR abs/1806.05805 (2018)
Coauthor Index
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