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 | 433 | 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 | 399 | 2019 |
A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification S Ryu, Y Kwon, WY Kim Chemical science 10 (36), 8438-8446, 2019 | 164* | 2019 |
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 | 118* | 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 | 80 | 2019 |
Hit and lead discovery with explorative rl and fragment-based molecule generation S Yang, D Hwang, S Lee, S Ryu, SJ Hwang Advances in Neural Information Processing Systems 34, 7924-7936, 2021 | 67 | 2021 |
Comprehensive study on molecular supervised learning with graph neural networks D Hwang, S Yang, Y Kwon, KH Lee, G Lee, H Jo, S Yoon, S Ryu Journal of Chemical Information and Modeling 60 (12), 5936-5945, 2020 | 48* | 2020 |
Effects of the locality of a potential derived from hybrid density functionals on Kohn–Sham orbitals and excited states J Kim, K Hong, SY Hwang, S Ryu, S Choi, WY Kim Physical Chemistry Chemical Physics 19 (15), 10177-10186, 2017 | 21 | 2017 |
Deeply learning molecular structure-property relationships using attentionand gate-augmented graph convolutional network S Ryu, J Lim, SH Hong, WY Kim arXiv preprint arXiv:1805.10988, 2018 | 10 | 2018 |
Update to ACE‐molecule: Projector augmented wave method on lagrange‐sinc basis set S Kang, S Ryu, S Choi, J Kim, K Hong, WY Kim International Journal of Quantum Chemistry 116 (8), 644-650, 2016 | 10 | 2016 |
Supersampling method for efficient grid-based electronic structure calculations S Ryu, S Choi, K Hong, WY Kim The Journal of Chemical Physics 144 (9), 2016 | 9 | 2016 |
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning S Ryu, S Lee arXiv preprint arXiv:2210.07145, 2022 | 4 | 2022 |
Galaxydock-dl: Protein–ligand docking by global optimization and neural network energy C Lee, J Won, S Ryu, J Yang, N Jung, H Park, C Seok Journal of Chemical Theory and Computation 20 (16), 7370-7382, 2024 | 2 | 2024 |
CSearch: chemical space search via virtual synthesis and global optimization H Kim, S Ryu, N Jung, J Yang, C Seok Journal of Cheminformatics 16 (1), 1-13, 2024 | | 2024 |
Understanding active learning of molecular docking and its applications J Kim, J Nam, S Ryu arXiv preprint arXiv:2406.12919, 2024 | | 2024 |
Performance of Range-Separated Hybrid Functional with Krieger-Li-Iafrate Potential for Molecular Excitation Energies K Sungwoo, J Kim, S Choi, JAE LIM, SY Hwang, S Ryu, WY Kim 11th Triennial Congress of the World Association of Theoretical and …, 2017 | | 2017 |
Importance of local exact exchange potential in hybrid functionals for accurate excited states J Kim, K Hong, SY Hwang, S Ryu, S Choi, WY Kim arXiv preprint arXiv:1610.09113, 2016 | | 2016 |
Supersampling double grid method to improve accuracy of real space electronic structure calculation S Ryu, S Choi, KW Hong, WY Kim IUPAC-2015, 2015 | | 2015 |