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. - Woo Youn Kim; Kwang S. Kim (J. Comput. Chem., 2008)

Performance of heterogeneous computing with graphics processing unit and many integrated core for hartree potential calculations on a numerical grid. - Sunghwan Choi; Oh-Kyoung Kwon; Jaewook Kim; Woo Youn Kim (J. Comput. Chem., 2016)

Deeply learning molecular structure-property relationships using graph attention neural network. - Seongok Ryu; Jaechang Lim; Woo Youn Kim (CoRR, 2018)

Kohn-Sham approach for fast hybrid density functional calculations in real-space numerical grid methods. - Jaewook Kim; Sungwoo Kang; Jaechang Lim; Sang-Yeon Hwang; Woo Youn Kim (Comput. Phys. Commun., 2018)

Molecular generative model based on conditional variational autoencoder for de novo molecular design. - Jaechang Lim; Seongok Ryu; Jin Woo Kim; Woo Youn Kim (J. Cheminformatics, 2018)

Uncertainty quantification of molecular property prediction with Bayesian neural networks. - Seongok Ryu; Yongchan Kwon; Woo Youn Kim (CoRR, 2019)

Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks. - Jaechang Lim; Seongok Ryu; Kyubyong Park; Yo Joong Choe; Jiyeon Ham; Woo Youn Kim (CoRR, 2019)

Uncertainty quantification of molecular property prediction using Bayesian neural network models. - Seongok Ryu; Yongchan Kwon; Woo Youn Kim (CoRR, 2019)

Scaffold-based molecular design using graph generative model. - Jaechang Lim; Sang-Yeon Hwang; Seungsu Kim; Seokhyun Moon; Woo Youn Kim (CoRR, 2019)

Molecular Generative Model Based On Adversarially Regularized Autoencoder. - Seung Hwan Hong; Jaechang Lim; Seongok Ryu; Woo Youn Kim (CoRR, 2019)

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. - Jaechang Lim; Seongok Ryu; Kyubyong Park; Yo Joong Choe; Jiyeon Ham; Woo Youn Kim (J. Chem. Inf. Model., 2019)

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions. - Seokhyun Moon; Wonho Zhung; Soojung Yang; Jaechang Lim; Woo Youn Kim (CoRR, 2020)

Molecular Generative Model Based on an Adversarially Regularized Autoencoder. - Seung Hwan Hong; Seongok Ryu; Jaechang Lim; Woo Youn Kim (J. Chem. Inf. Model., 2020)

Fragment-based molecular generative model with high generalization ability and synthetic accessibility. - Seonghwan Seo; Jaechang Lim; Woo Youn Kim (CoRR, 2021)

GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising. - Hyeonsu Kim; Jeheon Woo; Seonghwan Kim 004; Seokhyun Moon; Jun Hyeong Kim; Woo Youn Kim (NeurIPS, 2023)

Predicting quantum chemical property with easy-to-obtain geometry via positional denoising. - Hyeonsu Kim; Jeheon Woo; Seonghwan Kim 004; Seokhyun Moon; Jun Hyeong Kim; Woo Youn Kim (CoRR, 2023)

A 2D Graph-Based Generative Approach For Exploring Transition States Using Diffusion Model. - Seonghwan Kim 004; Jeheon Woo; Woo Youn Kim (CoRR, 2023)

PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening. - Seokhyun Moon; Sang-Yeon Hwang; Jaechang Lim; Woo Youn Kim (CoRR, 2023)

C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems. - Sehan Lee; Jaechang Lim; Woo Youn Kim (CoRR, 2023)

PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling. - Seonghwan Seo; Woo Youn Kim (CoRR, 2023)

pyMCD: Python package for searching transition states via the multicoordinate driven method. - Kyunghoon Lee; Jun Hyeong Kim; Woo Youn Kim (Comput. Phys. Commun., 2023)

NCIDiff: Non-covalent Interaction-generative Diffusion Model for Improving Reliability of 3D Molecule Generation Inside Protein Pocket. - Joongwon Lee; Wonho Zhung; Woo Youn Kim (CoRR, 2024)

Collective Variable Free Transition Path Sampling with Generative Flow Network. - Kiyoung Seong; Seonghyun Park 004; Seonghwan Kim 004; Woo Youn Kim; Sungsoo Ahn (CoRR, 2024)

Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy. - Jeheon Woo; Seonghwan Kim 004; Jun Hyeong Kim; Woo Youn Kim (CoRR, 2024)

Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery. - Hyeonsu Kim; Kyunghoon Lee; Jun Hyeong Kim; Woo Youn Kim (J. Chem. Inf. Model., 2024)

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. - Hyeongwoo Kim; Kyunghoon Lee; Chansu Kim; Jaechang Lim; Woo Youn Kim (J. Chem. Inf. Model., 2024)

TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design. - Tony Shen; Seonghwan Seo; Grayson Lee; Mohit Pandey; Jason R. Smith 003; Artem Cherkasov; Woo Youn Kim; Martin Ester (Trans. Mach. Learn. Res., 2024)

Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation. - Jun Hyeong Kim; Seonghwan Kim 004; Seokhyun Moon; Hyeongwoo Kim; Jeheon Woo; Woo Youn Kim (ICLR, 2025)

Generative Flows on Synthetic Pathway for Drug Design. - Seonghwan Seo; Minsu Kim 004; Tony Shen; Martin Ester; Jinkyoo Park; Sungsoo Ahn; Woo Youn Kim (ICLR, 2025)

Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers. - Kiyoung Seong; Seonghyun Park 004; Seonghwan Kim 004; Woo Youn Kim; Sungsoo Ahn (ICLR, 2025)

Compositional Flows for 3D Molecule and Synthesis Pathway Co-design. - Tony Shen; Seonghwan Seo; Ross Irwin; Kieran Didi; Simon Olsson; Woo Youn Kim; Martin Ester (CoRR, 2025)

HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model. - Jiyeon Han; Wonho Zhung; Insoo Jang; Joongwon Lee; Min Ji Kang; Timothy Dain Lee; Seung Jun Kwack; Kyu-Bong Kim; Daehee Hwang; Byungwook Lee; Hyung Sik Kim; Woo Youn Kim; et al. (J. Cheminformatics, 2025)