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Journal Articles
- 2024
[j1]Zachary J. Gale-Day
, Laura Shub, Kangway V. Chuang, Michael J. Keiser:
Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks. J. Chem. Inf. Model. 64(14): 5439-5450 (2024)
Conference and Workshop Papers
- 2023
- [c1]Nathaniel Lee Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. ICML 2023: 7939-7959
Informal and Other Publications
- 2025
- [i7]Karina Zadorozhny, Kangway V. Chuang, Bharath Sathappan, Ewan Wallace, Vishnu Sresht, Colin A. Grambow:
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction. CoRR abs/2501.09103 (2025) - 2024
- [i6]Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang, Gabriele Scalia, Tommaso Biancalani:
Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction. CoRR abs/2412.13478 (2024) - 2023
- [i5]Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia:
Improving Graph Generation by Restricting Graph Bandwidth. CoRR abs/2301.10857 (2023) - [i4]Colin A. Grambow, Hayley Weir, Christian N. Cunningham, Tommaso Biancalani, Kangway V. Chuang:
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning. CoRR abs/2305.08057 (2023) - [i3]Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant, Alex M. Tseng, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang:
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion. CoRR abs/2305.19800 (2023) - 2022
- [i2]Austin Atsango, Nathaniel L. Diamant, Ziqing Lu, Tommaso Biancalani, Gabriele Scalia, Kangway V. Chuang:
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning. CoRR abs/2211.02130 (2022) - 2020
- [i1]Kangway V. Chuang, Michael J. Keiser:
Attention-Based Learning on Molecular Ensembles. CoRR abs/2011.12820 (2020)
Coauthor Index
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