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 • 5 Oct 2023 • Tony Shen, Seonghwan Seo, Grayson Lee, Mohit Pandey, Jason R Smith, Artem Cherkasov, Woo Youn Kim, Martin Ester
Searching the vast chemical space for drug-like and synthesizable molecules with high binding affinity to a protein pocket is a challenging task in drug discovery.
2 code implementations • 1 Oct 2023 • Seonghwan Seo, Woo Youn Kim
Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time.
1 code implementation • 27 Sep 2023 • Sehan Lee, Jaechang Lim, Woo Youn Kim
Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology.
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.
1 code implementation • 20 Apr 2023 • SeongHwan Kim, Jeheon Woo, Woo Youn Kim
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics.
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.
2 code implementations • 25 Nov 2021 • Seonghwan Seo, Jaechang Lim, Woo Youn Kim
The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method.
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 • 13 Nov 2019 • Seung Hwan Hong, Jaechang Lim, Seongok Ryu, Woo Youn Kim
All models reported so far are based on either variational autoencoder (VAE) or generative adversarial network (GAN).
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
no code implementations • 17 Apr 2019 • Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design.
no code implementations • 20 Mar 2019 • Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Deep neural networks have outperformed existing machine learning models in various molecular applications.
no code implementations • 19 Nov 2018 • Seongok Ryu, Yongchan Kwon, Woo Youn Kim
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions.
1 code implementation • 15 Jun 2018 • Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design.
1 code implementation • 28 May 2018 • Seongok Ryu, Jaechang Lim, Seung Hwan Hong, Woo Youn Kim
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery.