| 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 | 129 | 2022 |
| 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 | 64 | 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 | 30 | 2020 |
| Chemically Transferable Generative Backmapping of Coarse-Grained Proteins S Yang, R Gómez-Bombarelli ICML 2023; arXiv preprint arXiv:2303.01569, 2023 | 19 | 2023 |
| A comprehensive study on the prediction reliability of graph neural networks for virtual screening S Yang, KH Lee, S Ryu arXiv preprint arXiv:2003.07611, 2020 | 10 | 2020 |
| Learning collective variables for protein folding with labeled data augmentation through geodesic interpolation S Yang, J Nam, JCB Dietschreit, R Gómez-Bombarelli JCTC; arXiv preprint arXiv:2402.01542, 2024 | 6* | 2024 |
| Regularized indirect learning improves phage display ligand discovery JS Brown, Y Tseo, MA Lee, JYK Wong, S Yang, Y Cho, CR Kim, A Loas, ... | 1 | 2023 |
| Flow Matching for Accelerated Simulation of Atomic Transport in Materials J Nam, S Liu, G Winter, KJ Jun, S Yang, R Gómez-Bombarelli arXiv preprint arXiv:2410.01464, 2024 | | 2024 |
| Probing the Embedding Space of Protein Foundation Models through Intrinsic Dimension Analysis S Yang, J Nam, T Perez, J Song, X Du, R Gomez-Bombarelli NeurIPS 2024 Workshop on AI for New Drug Modalities, 0 | | |
