Search Results - "Ryu, Seongok"

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

“…We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple…”
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A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification by Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

“…Deep neural networks have been increasingly used in various chemical fields. In the nature of a data-driven approach, their performance strongly depends on…”
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Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation by Lim, Jaechang, Ryu, Seongok, Park, Kyubyong, Choe, Yo Joong, Ham, Jiyeon, Kim, Woo Youn

“…We propose a novel deep learning approach for predicting drug–target interaction using a graph neural network. We introduce a distance-aware graph attention…”
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Molecular Generative Model Based on an Adversarially Regularized Autoencoder by Hong, Seung Hwan, Ryu, Seongok, Lim, Jaechang, Kim, Woo Youn

“…Deep generative models are attracting great attention as a new promising approach for molecular design. A variety of models reported so far are based on either…”
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GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy by Lee, Changsoo, Won, Jonghun, Ryu, Seongok, Yang, Jinsol, Jung, Nuri, Park, Hahnbeom, Seok, Chaok

“…With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly…”
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A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc01992h by Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

“…Deep neural networks have been increasingly used in various chemical fields. Here, we show that Bayesian inference enables more reliable prediction with…”
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Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks by Hwang, Doyeong, Yang, Soojung, Kwon, Yongchan, Lee, Kyung Hoon, Lee, Grace, Jo, Hanseok, Yoon, Seyeol, Ryu, Seongok

“…This work considers strategies to develop accurate and reliable graph neural networks (GNNs) for molecular property predictions. Prediction performance of GNNs…”
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Update to ACE-molecule: Projector augmented wave method on lagrange-sinc basis set by Kang, Sungwoo, Ryu, Seongok, Choi, Sunghwan, Kim, Jaewook, Hong, Kwangwoo, Kim, Woo Youn

“…The projector augmented wave (PAW) method was implemented in a quantum chemistry package that uses Lagrange‐sinc basis set, namely ACE‐Molecule. Its numerical…”
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Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning by Ryu, Seongok, Lee, Sumin

“…In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico…”
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Understanding active learning of molecular docking and its applications by Kim, Jeonghyeon, Nam, Juno, Ryu, Seongok

“…With the advancing capabilities of computational methodologies and resources, ultra-large-scale virtual screening via molecular docking has emerged as a…”
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A comprehensive study on the prediction reliability of graph neural networks for virtual screening by Yang, Soojung, Lee, Kyung Hoon, Ryu, Seongok

“…Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in…”
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Uncertainty quantification of molecular property prediction with Bayesian neural networks by Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

“…Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to…”
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Uncertainty quantification of molecular property prediction using Bayesian neural network models by Ryu, Seongok, Kwon, Yongchan, Kim, Woo Youn

“…In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule…”
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A benchmark study on reliable molecular supervised learning via Bayesian learning by Hwang, Doyeong, Lee, Grace, Jo, Hanseok, Yoon, Seyoul, Ryu, Seongok

“…Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs…”
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Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation by Yang, Soojung, Hwang, Doyeong, Lee, Seul, Ryu, Seongok, Hwang, Sung Ju

“…Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A…”
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Molecular Generative Model Based On Adversarially Regularized Autoencoder by Hong, Seung Hwan, Lim, Jaechang, Ryu, Seongok, Kim, Woo Youn

“…Deep generative models are attracting great attention as a new promising approach for molecular design. All models reported so far are based on either…”
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Molecular generative model based on conditional variational autoencoder for de novo molecular design by Lim, Jaechang, Ryu, Seongok, Kim, Jin Woo, Kim, Woo Youn

“…We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple…”
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Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network by Ryu, Seongok, Lim, Jaechang, Hong, Seung Hwan, Kim, Woo Youn

“…Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable…”
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Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks by Lim, Jaechang, Ryu, Seongok, Park, Kyubyong, Choe, Yo Joong, Ham, Jiyeon, Kim, Woo Youn

“…Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design. For the purpose, we propose a novel approach for predicting DTI…”
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Importance of local exact exchange potential in hybrid functionals for accurate excited states by Kim, Jaewook, Hong, Kwangwoo, Hwang, Sang-Yeon, Ryu, Seongok, Choi, Sunghwan, Kim, Woo Youn

“…Density functional theory has been an essential analysis tool for both theoretical and experimental chemists since accurate hybrid functionals were developed…”
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