95 citations · 214 across the 9 of their papers we have counts for
9 papers
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
Seokhyun Moon, Wonho Zhung, Soojung Yang +2
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insuff…
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…