| A deep learning approach to antibiotic discovery JM Stokes, K Yang, K Swanson, W Jin, A Cubillos-Ruiz, NM Donghia, ... Cell 180 (4), 688-702. e13, 2020 | 1653 | 2020 |
| Junction Tree Variational Autoencoder for Molecular Graph Generation W Jin, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2018 | 1441 | 2018 |
| Analyzing learned molecular representations for property prediction K Yang, K Swanson, W Jin, C Coley, P Eiden, H Gao, A Guzman-Perez, ... Journal of chemical information and modeling 59 (8), 3370-3388, 2019 | 1267 | 2019 |
| A graph-convolutional neural network model for the prediction of chemical reactivity CW Coley, W Jin, L Rogers, TF Jamison, TS Jaakkola, WH Green, ... Chemical science 10 (2), 370-377, 2019 | 628 | 2019 |
| Predicting organic reaction outcomes with weisfeiler-lehman network W Jin, C Coley, R Barzilay, T Jaakkola Advances in neural information processing systems 30, 2017 | 306 | 2017 |
| Hierarchical Generation of Molecular Graphs using Structural Motifs W Jin, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2020 | 305* | 2020 |
| Learning Multimodal Graph-to-Graph Translation for Molecular Optimization W Jin, K Yang, R Barzilay, T Jaakkola International Conference on Learning Representations (ICLR), 2018 | 276 | 2018 |
| Multi-Objective Molecule Generation using Interpretable Substructures W Jin, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2020 | 204* | 2020 |
| Generative models for molecular discovery: Recent advances and challenges C Bilodeau, W Jin, T Jaakkola, R Barzilay, KF Jensen Wiley Interdisciplinary Reviews: Computational Molecular Science 12 (5), e1608, 2022 | 149 | 2022 |
| Deriving neural architectures from sequence and graph kernels T Lei, W Jin, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2017 | 148 | 2017 |
| Deep learning identifies synergistic drug combinations for treating COVID-19 W Jin, JM Stokes, RT Eastman, Z Itkin, AV Zakharov, JJ Collins, ... Proceedings of the National Academy of Sciences 118 (39), e2105070118, 2021 | 111 | 2021 |
| Iterative refinement graph neural network for antibody sequence-structure co-design W Jin, J Wohlwend, R Barzilay, T Jaakkola International Conference on Learning Representations (ICLR), 2021 | 100 | 2021 |
| Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii G Liu, DB Catacutan, K Rathod, K Swanson, W Jin, JC Mohammed, ... Nature Chemical Biology 19 (11), 1342-1350, 2023 | 89 | 2023 |
| Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement W Jin, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2022 | 43* | 2022 |
| Enforcing Predictive Invariance across Structured Biomedical Domains W Jin, R Barzilay, T Jaakkola arXiv preprint arXiv:2006.03908, 2020 | 40* | 2020 |
| Functional Transparency for Structured Data: a Game-Theoretic Approach GH Lee, W Jin, D Alvarez-Melis, TS Jaakkola International Conference on Machine Learning (ICML), 2019 | 23 | 2019 |
| Improving Molecular Design by Stochastic Iterative Target Augmentation K Yang, W Jin, K Swanson, R Barzilay, T Jaakkola International Conference on Machine Learning (ICML), 2020 | 21 | 2020 |
| Discovery of a structural class of antibiotics with explainable deep learning F Wong, EJ Zheng, JA Valeri, NM Donghia, MN Anahtar, S Omori, A Li, ... Nature 626 (7997), 177-185, 2024 | 18 | 2024 |
| Mol2Image: improved conditional flow models for molecule to image synthesis K Yang, S Goldman, W Jin, AX Lu, R Barzilay, T Jaakkola, C Uhler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 15* | 2021 |
| Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back BA Koscher, RB Canty, MA McDonald, KP Greenman, CJ McGill, ... Science 382 (6677), eadi1407, 2023 | 14 | 2023 |
Regina BarzilayProfessor of EECS, MITcsail.mit.edu의 이메일 확인됨
