Search Results for author: Woo Youn Kim

Found 14 papers, 10 papers with code

PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling

1 code implementation1 Oct 2023 Seonghwan Seo, Woo Youn Kim

As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging.

Drug Discovery Graph Matching

C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems

1 code implementation27 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.

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

Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs

1 code implementation20 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.

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.


Fragment-based molecular generative model with high generalization ability and synthetic accessibility

2 code implementations25 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.

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

Molecular Generative Model Based On Adversarially Regularized Autoencoder

1 code implementation13 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).

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

Uncertainty quantification of molecular property prediction using Bayesian neural network models

no code implementations19 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.

Molecular Property Prediction Property Prediction

Molecular generative model based on conditional variational autoencoder for de novo molecular design

1 code implementation15 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.

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