151 citations · 160 across the 7 of their papers we have counts for
7 papers
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
Joongwon Lee, Seonghwan Kim, Seokhyun Moon +2
We introduce FragFM, a novel hierarchical framework via fragment-level discrete flow matching for efficient molecular graph generation. FragFM generates molecules at the fragment l…
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…
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung +3
Optimizing molecular properties while preserving biological activity is a central challenge in drug design. Bioisosteric replacement, which substitutes a molecular fragment with a…
PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening
Seokhyun Moon, Sang-Yeon Hwang, Jaechang Lim +1
Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target…
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising
Hyeonsu Kim, Jeheon Woo, Seonghwan Kim +3
As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tas…
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
Seokhyun Moon, Wonho Zhung, Soojung Yang +2
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insuff…
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…