no code implementations • 2 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.
no code implementations • 5 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.
1 code implementation • 3 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.
no code implementations • 28 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.
1 code implementation • 22 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.
1 code implementation • 31 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.
Ranked #2 on Molecular Graph Generation on InterBioScreen