EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. - Esther Heid; Samuel Goldman; Karthik Sankaranarayanan; Connor W. Coley; Christoph Flamm; William H. Green Jr. (J. Chem. Inf. Model., 2021)

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. - Giovanni Bolcato; Esther Heid; Jonas Boström (J. Chem. Inf. Model., 2022)

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. - Esther Heid; William H. Green Jr. (J. Chem. Inf. Model., 2022)

Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications. - Esther Heid; Jiannan Liu; Andrea Aude; William H. Green Jr. (J. Chem. Inf. Model., 2022)

Characterizing Uncertainty in Machine Learning for Chemistry. - Esther Heid; Charles McGill; Florence H. Vermeire; William H. Green Jr. (J. Chem. Inf. Model., 2023)

Spatially Resolved Uncertainties for Machine Learning Potentials. - Esther Heid; Johannes Schörghuber; Ralf Wanzenböck; Georg K. H. Madsen (J. Chem. Inf. Model., 2024)

LoGAN: local generative adversarial network for novel structure prediction. - Péter Kovács; Esther Heid; Jasper De Landsheere; Georg K. H. Madsen (Mach. Learn. Sci. Technol., 2024)

Chemprop: A Machine Learning Package for Chemical Property Prediction. - Esther Heid; Kevin P. Greenman; Yunsie Chung; Shih-Cheng Li; David E. Graff; Florence H. Vermeire; Haoyang Wu; William H. Green Jr.; Charles McGill (J. Chem. Inf. Model., 2024)