Staff information

Kate Schultz

Data Scientist
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Hollerbach A.L., Y.M. Ibrahim, V.S. Lin, K.J. Schultz, A.P. Huntley, P.B. Armentrout, and T.O. Metz, et al. 2024. "Identification of Unique Fragmentation Patterns of Fentanyl Analog Protomers using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry." Journal of the American Society for Mass Spectrometry 35, no. 4:793-803. PNNL-SA-192943. doi:10.1021/jasms.4c00049

Joshi R., K.J. Schultz, J.W. Wilson, A. Kruel, R.A. Varikoti, C. Kombala Nanayakkara Thambiliya, and D.W. Kneller, et al. 2023. "AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2." Journal of Chemical Information and Modeling 63, no. 5:1438-1453. PNNL-SA-176233. doi:10.1021/acs.jcim.2c01377

Varikoti R.A., K.J. Schultz, M. Zhou, C. Kombala Nanayakkara Thambiliya, K.R. Brandvold, A. Kruel, and N. Kumar. 2022. Machine Learning-driven Molecular Design for Therapeutic Discovery. PNNL-33349. Richland, WA: Pacific Northwest National Laboratory. Machine Learning-driven Molecular Design for Therapeutic Discovery
Zhou M., K.J. Schultz, J.W. Wilson, A. Kruel, S.M. Thibert, C.C. Bracken, and D.J. Orton, et al. 2022. High-throughput native mass spectrometry as experimental validation for in silico drug design. PNNL-33361. Richland, WA: Pacific Northwest National Laboratory. High-throughput native mass spectrometry as experimental validation for in silico drug design

Schultz K.J., S.M. Colby, V.S. Lin, A.T. Wright, and R.S. Renslow. 2021. "Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential alpha2a Adrenoceptor Agonists." Journal of Chemical Information and Modeling 61, no. 1:481-492. PNNL-SA-155859. doi:10.1021/acs.jcim.0c01019
Schultz K.J., S.M. Colby, Y. Yesiltepe, J. Nunez, M.Y. McGrady, and R.S. Renslow. 2021. "Application and Assessment of Deep Learning for the Generation of Potential NMDA Receptor Antagonists." Physical Chemistry Chemical Physics 23, no. 2:1197-1214. PNNL-SA-152120. doi:10.1039/D0CP03620J