Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules. - James L. McDonagh; Neetika Nath; Luna De Ferrari; Tanja Van Mourik; John B. O. Mitchell (J. Chem. Inf. Model., 2014)

Are the Sublimation Thermodynamics of Organic Molecules Predictable? - James L. McDonagh; David S. Palmer; Tanja Van Mourik; John B. O. Mitchell (J. Chem. Inf. Model., 2016)

Improving Performance and Energy Efficiency on OpenPower Systems Using Scalable Hardware-Software Co-design. - Milos Puzovic; Vadim V. Elisseev; Kirk E. Jordan; James L. McDonagh; Alexander Harrison; Robert Sawko (ISC Workshops, 2018)

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields. - James L. McDonagh; Ardita Shkurti; David J. Bray; Richard L. Anderson 003; Edward O. Pyzer-Knapp (J. Chem. Inf. Model., 2019)

Fast, Transparent, and High-Fidelity Memoization Cache-Keys for Computational Workflows. - Vassilis Vassiliadis; Michael A. Johnston; James L. McDonagh (SCC, 2022)

Machine Guided Discovery of Novel Carbon Capture Solvents. - James L. McDonagh; Benjamin H. Wunsch; Stamatia Zavitsanou; Alexander Harrison; Bruce Elmegreen; Stacey Gifford; Theodore van Kessel; Flaviu S. Cipcigan (CoRR, 2023)

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models. - Jonathan G. M. Conn; James W. Carter; Justin J. A. Conn; Vigneshwari Subramanian; Andrew Baxter; Ola Engkvist; Antonio Llinàs; Ekaterina Ratkova; Stephen D. Pickett; James L. McDonagh; David S. Palmer (J. Chem. Inf. Model., 2023)