| Molecular generative model based on conditional variational autoencoder for de novo molecular design J Lim, S Ryu, JW Kim, WY Kim Journal of cheminformatics 10, 1-9, 2018 | 437 | 2018 |
| Predicting drug–target interaction using a novel graph neural network with 3D structure-embedded graph representation J Lim, S Ryu, K Park, YJ Choe, J Ham, WY Kim Journal of chemical information and modeling 59 (9), 3981-3988, 2019 | 401 | 2019 |
| Scaffold-based molecular design with a graph generative model J Lim, SY Hwang, S Moon, S Kim, WY Kim Chemical science 11 (4), 1153-1164, 2020 | 179 | 2020 |
| PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions S Moon, W Zhung, S Yang, J Lim, WY Kim Chemical Science 13 (13), 3661-3673, 2022 | 133 | 2022 |
| Deeply learning molecular structure-property relationships using attention-and gate-augmented graph convolutional network S Ryu, J Lim, SH Hong, WY Kim arXiv preprint arXiv:1805.10988, 2018 | 122* | 2018 |
| Molecular generative model based on an adversarially regularized autoencoder SH Hong, S Ryu, J Lim, WY Kim Journal of chemical information and modeling 60 (1), 29-36, 2019 | 82 | 2019 |
| Study of Li adsorption on graphdiyne using hybrid DFT calculations J Kim, S Kang, J Lim, WY Kim ACS applied materials & interfaces 11 (3), 2677-2683, 2018 | 46 | 2018 |
| Drug-likeness scoring based on unsupervised learning K Lee, J Jang, S Seo, J Lim, WY Kim Chemical Science 13 (2), 554-565, 2022 | 40 | 2022 |
| Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly (Adv. Sci. 8/2023) S Seo, J Lim, WY Kim Advanced Science 10 (8), 2023 | 25 | 2023 |
| PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening S Moon, SY Hwang, J Lim, WY Kim Digital Discovery 3 (2), 287-299, 2024 | 15* | 2024 |
| Homochiral supramolecular thin film from self-assembly of achiral triarylamine molecules by circularly polarized light C Park, J Lee, T Kim, J Lim, J Park, WY Kim, SY Kim Molecules 25 (2), 402, 2020 | 14 | 2020 |
| ACE-Molecule: An open-source real-space quantum chemistry package S Kang, J Woo, J Kim, H Kim, Y Kim, J Lim, S Choi, WY Kim The Journal of Chemical Physics 152 (12), 2020 | 12 | 2020 |
| Outstanding performance of configuration interaction singles and doubles using exact exchange Kohn-Sham orbitals in real-space numerical grid method J Lim, S Choi, J Kim, WY Kim The Journal of Chemical Physics 145 (22), 2016 | 10 | 2016 |
| DFRscore: deep learning-based scoring of synthetic complexity with drug-focused retrosynthetic analysis for high-throughput virtual screening H Kim, K Lee, C Kim, J Lim, WY Kim Journal of Chemical Information and Modeling 64 (7), 2432-2444, 2023 | 7 | 2023 |
| Non-empirical atomistic dipole-interaction-model for quantum plasmon simulation of nanoparticles J Lim, S Kang, J Kim, WY Kim, S Ryu Scientific Reports 7 (1), 15775, 2017 | 7 | 2017 |
| Improvement of initial guess via grid‐cutting for efficient grid‐based density functional calculations J Lim, S Choi, S Kang, J Kim, K Hong, WY Kim International Journal of Quantum Chemistry 116 (19), 1397-1403, 2016 | 7 | 2016 |
| Kohn–Sham approach for fast hybrid density functional calculations in real-space numerical grid methods J Kim, S Kang, J Lim, SY Hwang, WY Kim Computer Physics Communications 230, 21-26, 2018 | 5 | 2018 |
| DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties H Kim, S Moon, W Zhung, J Lim, WY Kim arXiv preprint arXiv:2403.02706, 2024 | 1 | 2024 |
| DeepBioisostere: Deep Learning-based Bioisosteric Replacements for Optimization of Multiple Molecular Properties WY Kim, H Kim, S Moon, W Zhung, J Lim | | 2024 |
| DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Delicate Control of Multiple Properties WY Kim, H Kim, S Moon, W Zhung, J Lim 2024 년도 한국화학공학회 봄 총회 및 학술대회, 2024 | | 2024 |
