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Journal Articles
- 2024
[j9]Hyeonsu Kim, Kyunghoon Lee, Jun Hyeong Kim
, Woo Youn Kim:
Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery. J. Chem. Inf. Model. 64(3): 677-689 (2024)- [j8]Hyeongwoo Kim, Kyunghoon Lee, Chansu Kim, Jaechang Lim, Woo Youn Kim:
DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. J. Chem. Inf. Model. 64(7): 2432-2444 (2024) - 2023
- [j7]Kyunghoon Lee, Jun Hyeong Kim, Woo Youn Kim:
pyMCD: Python package for searching transition states via the multicoordinate driven method. Comput. Phys. Commun. 291: 108831 (2023) - 2020
- [j6]Seung Hwan Hong, Seongok Ryu, Jaechang Lim, Woo Youn Kim:
Molecular Generative Model Based on an Adversarially Regularized Autoencoder. J. Chem. Inf. Model. 60(1): 29-36 (2020) - 2019
- [j5]Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim:
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. J. Chem. Inf. Model. 59(9): 3981-3988 (2019) - 2018
- [j4]Jaewook Kim, Sungwoo Kang, Jaechang Lim, Sang-Yeon Hwang, Woo Youn Kim:
Kohn-Sham approach for fast hybrid density functional calculations in real-space numerical grid methods. Comput. Phys. Commun. 230: 21-26 (2018) - [j3]Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim:
Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminformatics 10(1): 31:1-31:9 (2018) - 2016
- [j2]Sunghwan Choi, Oh-Kyoung Kwon, Jaewook Kim, Woo Youn Kim:
Performance of heterogeneous computing with graphics processing unit and many integrated core for hartree potential calculations on a numerical grid. J. Comput. Chem. 37(24): 2193-2201 (2016) - 2008
- [j1]Woo Youn Kim, Kwang S. Kim:
Carbon nanotube, graphene, nanowire, and molecule-based electron and spin transport phenomena using the nonequilibrium Green's function method at the level of first principles theory. J. Comput. Chem. 29(7): 1073-1083 (2008)
Conference and Workshop Papers
- 2023
- [c1]Hyeonsu Kim, Jeheon Woo, Seonghwan Kim, Seokhyun Moon, Jun Hyeong Kim, Woo Youn Kim:
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising. NeurIPS 2023
Informal and Other Publications
- 2024
- [i17]Joongwon Lee, Wonho Zhung, Woo Youn Kim:
NCIDiff: Non-covalent Interaction-generative Diffusion Model for Improving Reliability of 3D Molecule Generation Inside Protein Pocket. CoRR abs/2405.16861 (2024) - [i16]Kiyoung Seong, Seonghyun Park, Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn:
Collective Variable Free Transition Path Sampling with Generative Flow Network. CoRR abs/2405.19961 (2024) - [i15]Jun Hyeong Kim, Seonghwan Kim, Seokhyun Moon, Hyeongwoo Kim, Jeheon Woo, Woo Youn Kim:
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation. CoRR abs/2410.01500 (2024) - 2023
- [i14]Hyeonsu Kim, Jeheon Woo, Seonghwan Kim, Seokhyun Moon, Jun Hyeong Kim, Woo Youn Kim:
Predicting quantum chemical property with easy-to-obtain geometry via positional denoising. CoRR abs/2304.03724 (2023) - [i13]Seonghwan Kim, Jeheon Woo, Woo Youn Kim:
A 2D Graph-Based Generative Approach For Exploring Transition States Using Diffusion Model. CoRR abs/2304.12233 (2023) - [i12]Seokhyun Moon, Sang-Yeon Hwang, Jaechang Lim, Woo Youn Kim:
PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening. CoRR abs/2307.01066 (2023) - [i11]Sehan Lee, Jaechang Lim, Woo Youn Kim:
C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems. CoRR abs/2309.15334 (2023) - [i10]Seonghwan Seo, Woo Youn Kim:
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling. CoRR abs/2310.00681 (2023) - 2021
- [i9]Seonghwan Seo, Jaechang Lim, Woo Youn Kim:
Fragment-based molecular generative model with high generalization ability and synthetic accessibility. CoRR abs/2111.12907 (2021) - 2020
- [i8]Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim:
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions. CoRR abs/2008.12249 (2020) - 2019
- [i7]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction with Bayesian neural networks. CoRR abs/1903.08375 (2019) - [i6]Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim:
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks. CoRR abs/1904.08144 (2019) - [i5]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction using Bayesian neural network models. CoRR abs/1905.06945 (2019) - [i4]Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, Woo Youn Kim:
Scaffold-based molecular design using graph generative model. CoRR abs/1905.13639 (2019) - [i3]Seung Hwan Hong, Jaechang Lim, Seongok Ryu, Woo Youn Kim:
Molecular Generative Model Based On Adversarially Regularized Autoencoder. CoRR abs/1912.05617 (2019) - 2018
- [i2]Seongok Ryu, Jaechang Lim, Woo Youn Kim:
Deeply learning molecular structure-property relationships using graph attention neural network. CoRR abs/1805.10988 (2018) - [i1]Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim:
Molecular generative model based on conditional variational autoencoder for de novo molecular design. CoRR abs/1806.05805 (2018)
Coauthor Index
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