Wengong Jin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Graph and Geometry Generative Modeling for Drug Discovery.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement.

[BibT_eX]

[DOI]

CoRR, 2022

Antibody-Antigen Docking and Design via Hierarchical Structure Refinement.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Graph Representation Learning for Drug Discovery.

[BibT_eX]

[DOI]

PhD thesis, 2021

Deep learning identifies synergistic drug combinations for treating COVID-19.

[BibT_eX]

[DOI]

Proc. Natl. Acad. Sci. USA, 2021

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models.

[BibT_eX]

[DOI]

CoRR, 2020

Improved Conditional Flow Models for Molecule to Image Synthesis.

[BibT_eX]

[DOI]

CoRR, 2020

Domain Extrapolation via Regret Minimization.

[BibT_eX]

[DOI]

CoRR, 2020

Adaptive Invariance for Molecule Property Prediction.

[BibT_eX]

[DOI]

CoRR, 2020

Composing Molecules with Multiple Property Constraints.

[BibT_eX]

[DOI]

CoRR, 2020

Improving Molecular Design by Stochastic Iterative Target Augmentation.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Multi-Objective Molecule Generation using Interpretable Substructures.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Hierarchical Generation of Molecular Graphs using Structural Motifs.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Correction to Analyzing Learned Molecular Representations for Property Prediction.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

Analyzing Learned Molecular Representations for Property Prediction.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules.

[BibT_eX]

[DOI]

CoRR, 2019

Are Learned Molecular Representations Ready For Prime Time?

[BibT_eX]

[DOI]

CoRR, 2019

Functional Transparency for Structured Data: a Game-Theoretic Approach.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization.

[BibT_eX]

[DOI]

CoRR, 2018

Junction Tree Variational Autoencoder for Molecular Graph Generation.

[BibT_eX]

[DOI]