Search Results for author: Seokhyun Moon

Found 6 papers, 3 papers with code

Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation

no code implementations2 Oct 2024 Jun Hyeong Kim, SeongHwan Kim, Seokhyun Moon, Hyeongwoo Kim, Jeheon Woo, Woo Youn Kim

Our approach extends Iterative Markovian Fitting to discrete domains, and we have proved its convergence to the SB.

DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties

no code implementations5 Mar 2024 Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, Jaechang Lim, Woo Youn Kim

Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties like drug-likeness.

PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening

1 code implementation3 Jul 2023 Seokhyun Moon, Sang-Yeon Hwang, Jaechang Lim, Woo Youn Kim

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 proteins.

Data Augmentation Drug Discovery +1

GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising

no code implementations28 Mar 2023 Hyeonsu Kim, Jeheon Woo, SeongHwan Kim, Seokhyun Moon, Jun Hyeong Kim, Woo Youn Kim

Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property.

Denoising

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

1 code implementation22 Aug 2020 Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim

Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs.

Drug Discovery

Scaffold-based molecular design using graph generative model

1 code implementation31 May 2019 Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn Kim

Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest.

Drug Discovery

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