Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction. - Gabriele Scalia; Colin A. Grambow; Barbara Pernici; Yi-Pei Li; William H. Green Jr. (CoRR, 2019)

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction. - Gabriele Scalia; Colin A. Grambow; Barbara Pernici; Yi-Pei Li; William H. Green Jr. (J. Chem. Inf. Model., 2020)

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation. - Mengjie Liu; Alon Grinberg Dana; Matthew S. Johnson; Mark J. Goldman; Agnes Jocher; A. Mark Payne; Colin A. Grambow; Kehang Han; Nathan W. Yee; Emily J. Mazeau; Katrin Blondal; Richard H. West; et al. (J. Chem. Inf. Model., 2021)

RMG Database for Chemical Property Prediction. - Matthew S. Johnson; Xiaorui Dong; Alon Grinberg Dana; Yunsie Chung; David Farina; Ryan J. Gillis; Mengjie Liu; Nathan W. Yee; Katrin Blondal; Emily J. Mazeau; Colin A. Grambow; A. Mark Payne; et al. (J. Chem. Inf. Model., 2022)

Investigating the Utility of Synthetic Data for Doctor-Patient Conversation Summarization. - Siyuan Chen; Colin A. Grambow; Mojtaba Kadkhodaie Elyaderani; Alireza Sadeghi; Federico Fancellu; Thomas Schaaf (INTERSPEECH, 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)

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)