Attention-Based Learning on Molecular Ensembles. - Kangway V. Chuang; Michael J. Keiser (CoRR, 2020)

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning. - Austin Atsango; Nathaniel L. Diamant; Ziqing Lu; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2022)

Improving Graph Generation by Restricting Graph Bandwidth. - Nathaniel Lee Diamant; Alex M. Tseng; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (ICML, 2023)

CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning. - Colin A. Grambow; Hayley Weir; Christian N. Cunningham; Tommaso Biancalani; Kangway V. Chuang (CoRR, 2023)

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion. - Colin A. Grambow; Hayley Weir; Nathaniel L. Diamant; Alex M. Tseng; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2023)

Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction. - Sepideh Maleki; Jan-Christian Huetter; Kangway V. Chuang; Gabriele Scalia; Tommaso Biancalani (CoRR, 2024)

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks. - Zachary J. Gale-Day; Laura Shub; Kangway V. Chuang; Michael J. Keiser (J. Chem. Inf. Model., 2024)

Score-based Explainability for Graph Representations. - Ehsan Hajiramezanali; Sepideh Maleki; Max W. Shen; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (Trans. Mach. Learn. Res., 2024)

Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction. - Karina Zadorozhny; Kangway V. Chuang; Bharath Sathappan; Ewan R. S. Wallace; Vishnu Sresht; Colin A. Grambow (CoRR, 2025)