4 citations · 10 across the 8 of their papers we have counts for
6 papers · 1 filter
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
Jeheon Woo, Seonghwan Kim, Jun Hyeong Kim +1
We introduce a framework for molecular structure optimization using denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Eu…
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Jun Hyeong Kim, Seonghwan Kim, Seokhyun Moon +3
Transporting between arbitrary distributions is a fundamental goal in generative modeling. Recently proposed diffusion bridge models provide a potential solution, but they rely on…
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
Kiyoung Seong, Seonghyun Park, Seonghwan Kim +2
Understanding transition pathways between two meta-stable states of a molecular system is crucial to advance drug discovery and material design. However, unbiased molecular dynamic…
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…
Fragment-based molecular generative model with high generalization ability and synthetic accessibility
Seonghwan Seo, Jaechang Lim, Woo Youn Kim
Deep generative models are attracting great attention for molecular design with desired properties. Most existing models generate molecules by sequentially adding atoms. This often…
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…