MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties

1 code implementation • 1 Feb 2022 • YeJi Kim, Yoonho Jeong, Jihoo Kim, Eok Kyun Lee, Won June Kim, Insung S. Choi

The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs.

InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation

no code implementations • 12 May 2020 • Hyeoncheol Cho, Eok Kyun Lee, Insung S. Choi

Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design.

Quantum Shift Register

1 code implementation • 19 Dec 2001 • Jae-weon Lee, Eok Kyun Lee, Jaewan Kim, Soonchil Lee

We consider a quantum circuit in which shift and rotation operations on qubits are performed by swap gates and controlled swap gates.

Quantum Physics