79 citations · 149 across the 11 of their papers we have counts for
11 papers
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning
Seongok Ryu, Sumin Lee
In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has…
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
Soojung Yang, Doyeong Hwang, Seul Lee +2
Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A molecular docking prog…
A benchmark study on reliable molecular supervised learning via Bayesian learning
Doyeong Hwang, Grace Lee, Hanseok Jo +2
Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpret…
A comprehensive study on the prediction reliability of graph neural networks for virtual screening
Soojung Yang, Kyung Hoon Lee, Seongok Ryu
Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in virtual screening, resea…
Molecular Generative Model Based On Adversarially Regularized Autoencoder
Seung Hwan Hong, Jaechang Lim, Seongok Ryu +1
Deep generative models are attracting great attention as a new promising approach for molecular design. All models reported so far are based on either variational autoencoder (VAE)…
Uncertainty quantification of molecular property prediction using Bayesian neural network models
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning c…
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Jaechang Lim, Seongok Ryu, Kyubyong Park +3
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 using a GNN that direct…
Uncertainty quantification of molecular property prediction with Bayesian neural networks
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisi…
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim +1
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties si…
Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
Seongok Ryu, Jaechang Lim, Seung Hwan Hong +1
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate th…
Importance of local exact exchange potential in hybrid functionals for accurate excited states
Jaewook Kim, Kwangwoo Hong, Sang-Yeon Hwang +3
Density functional theory has been an essential analysis tool for both theoretical and experimental chemists since accurate hybrid functionals were developed. Here we propose a loc…