26 citations · 69 across the 5 of their papers we have counts for
5 papers
Drug Discovery with Dynamic Goal-aware Fragments
Seul Lee, Seanie Lee, Kenji Kawaguchi +1
Fragment-based drug discovery is an effective strategy for discovering drug candidates in the vast chemical space, and has been widely employed in molecular generative models. Howe…
Exploring Chemical Space with Score-based Out-of-distribution Generation
Seul Lee, Jaehyeong Jo, Sung Ju Hwang
A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules that ma…
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang
Generating graph-structured data requires learning the underlying distribution of graphs. Yet, this is a challenging problem, and the previous graph generative methods either fail…
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…
Edge Representation Learning with Hypergraphs
Jaehyeong Jo, Jinheon Baek, Seul Lee +3
Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet…