151 citations · 241 across the 12 of their papers we have counts for
3 papers · 1 filter
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)…
Scaffold-based molecular design using graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim +2
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called…
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…