4 citations · 6 across the 5 of their papers we have counts for
5 papers
Exploring Synthesizable Chemical Space with Iterative Pathway Refinements
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effec…
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +6
Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present Generalis…
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragme…
Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship
Eunji Ko, Seul Lee, Minseon Kim +1
The goal of protein representation learning is to extract knowledge from protein databases that can be applied to various protein-related downstream tasks. Although protein sequenc…
A Simple and Scalable Representation for Graph Generation
Yunhui Jang, Seul Lee, Sungsoo Ahn
Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule…