5 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems
Sehan Lee, Jaechang Lim, Woo Youn Kim
Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology. In this work, we propose a deep neural network architecture f…
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…
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…