Deeply learning molecular structure-property relationships using graph attention neural network. - Seongok Ryu; Jaechang Lim; Woo Youn Kim (CoRR, 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)

Uncertainty quantification of molecular property prediction with Bayesian neural networks. - 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 using Bayesian neural network models. - Seongok Ryu; Yongchan Kwon; Woo Youn Kim (CoRR, 2019)

Molecular Generative Model Based On Adversarially Regularized Autoencoder. - Seung Hwan Hong; Jaechang Lim; Seongok Ryu; Woo Youn Kim (CoRR, 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)

A comprehensive study on the prediction reliability of graph neural networks for virtual screening. - Soojung Yang; Kyung-Hoon Lee; Seongok Ryu (CoRR, 2020)

A benchmark study on reliable molecular supervised learning via Bayesian learning. - Doyeong Hwang; Grace Lee; Hanseok Jo; Seyoul Yoon; Seongok Ryu (CoRR, 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)

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)

Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation. - Soojung Yang; Doyeong Hwang; Seul Lee; Seongok Ryu; Sung Ju Hwang (NeurIPS, 2021)

Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning. - Seongok Ryu; Sumin Lee (CoRR, 2022)

Understanding active learning of molecular docking and its applications. - Jeonghyeon Kim; Juno Nam; Seongok Ryu (CoRR, 2024)

CSearch: chemical space search via virtual synthesis and global optimization. - Hakjean Kim; Seongok Ryu; Nuri Jung; Jinsol Yang; Chaok Seok (J. Cheminformatics, 2024)