Molecular generative model based on conditional variational autoencoder for de novo molecular design von Lim, Jaechang, Ryu, Seongok, Kim, Jin Woo, Kim, Woo Youn

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Supersampling method for efficient grid-based electronic structure calculations von Ryu, Seongok, Choi, Sunghwan, Hong, Kwangwoo, Kim, Woo Youn

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A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification von Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

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Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation von Lim, Jaechang, Ryu, Seongok, Park, Kyubyong, Choe, Yo Joong, Ham, Jiyeon, Kim, Woo Youn

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Molecular Generative Model Based on an Adversarially Regularized Autoencoder von Hong, Seung Hwan, Ryu, Seongok, Lim, Jaechang, Kim, Woo Youn

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GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy von Lee, Changsoo, Won, Jonghun, Ryu, Seongok, Yang, Jinsol, Jung, Nuri, Park, Hahnbeom, Seok, Chaok

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CSearch: chemical space search via virtual synthesis and global optimization von Kim, Hakjean, Ryu, Seongok, Jung, Nuri, Yang, Jinsol, Seok, Chaok

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A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification† †Electronic supplementary information (ESI) available. See D... von Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

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Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks von Hwang, Doyeong, Yang, Soojung, Kwon, Yongchan, Lee, Kyung Hoon, Lee, Grace, Jo, Hanseok, Yoon, Seyeol, Ryu, Seongok

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Effects of the locality of a potential derived from hybrid density functionals on Kohn-Sham orbitals and excited states von Kim, Jaewook, Hong, Kwangwoo, Hwang, Sang-Yeon, Ryu, Seongok, Choi, Sunghwan, Kim, Woo Youn

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Effects of the locality of a potential derived from hybrid density functionals on Kohn-Sham orbitals and excited statesElectronic supplementary information (ESI) available: The ato... von Kim, Jaewook, Hong, Kwangwoo, Hwang, Sang-Yeon, Ryu, Seongok, Choi, Sunghwan, Kim, Woo Youn

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Update to ACE-molecule: Projector augmented wave method on lagrange-sinc basis set von Kang, Sungwoo, Ryu, Seongok, Choi, Sunghwan, Kim, Jaewook, Hong, Kwangwoo, Kim, Woo Youn

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Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning von Ryu, Seongok, Lee, Sumin

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Understanding active learning of molecular docking and its applications von Kim, Jeonghyeon, Nam, Juno, Ryu, Seongok

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Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning von Ryu, Seongok, Lee, Sumin

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Understanding active learning of molecular docking and its applications von Kim, Jeonghyeon, Nam, Juno, Ryu, Seongok

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A comprehensive study on the prediction reliability of graph neural networks for virtual screening von Yang, Soojung, Lee, Kyung Hoon, Ryu, Seongok

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Uncertainty quantification of molecular property prediction with Bayesian neural networks von Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

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Uncertainty quantification of molecular property prediction using Bayesian neural network models von Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

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A benchmark study on reliable molecular supervised learning via Bayesian learning von Hwang, Doyeong, Lee, Grace, Jo, Hanseok, Yoon, Seyoul, Ryu, Seongok

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