Data Scientist
Data Scientist

Biography

Kate Schultz is a data scientist in the Computational Biology group within the Pacific Northwest National Laboratory’s Earth and Biological Sciences Directorate. She conducts research and develops software to advance computational capabilities in the areas of in silico drug discovery, protein-ligand interactions, chemical space analysis, and computational toxicology. She collaborates with both academic groups and government agencies in these efforts.

Her research has been published in Physical Chemistry Chemical Physics, Journal of Chemical Information and Modeling, and Journal of Computer-Aided Molecular Design. She has presented her work at the American Chemical Society’s biannual conference, the Massachusetts Institute of Technology’s AI in Drug Discovery conference, the International Chemical Congress of Pacific Basin Societies, and at U.S. government agency-run conferences.

Research Interest

  • Bioinformatics
  • Cheminformatics
  • Computational chemistry
  • Data sciences
  • Drug discovery
  • High-performance computing
  • Machine learning
  • Molecular modeling

Education

  • MS in computer science, Northeastern University
  • BS in chemical engineering, University of Washington

Affiliations and Professional Service

  • Member, American Chemical Society
  • Member, WomiX (Women in Metabolomics)

Publications

2025

  • Rude C.I., J.N. Smith, R. Scott, K.J. Schultz, K.A. Anderson, and R.L. Tanguay. 2025. "A mixture parameterized biologically based dosimetry model to predict body burdens of polycyclic aromatic hydrocarbons in developmental zebrafish toxicity assays." Toxicological Sciences 205, no. 2:326-343. PNNL-SA-208758. doi:10.1093/toxsci/kfaf039

2024

  • 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
  • Rude C.I., L.B. Wilson, J.K. La Du, P.M. Lalli, S.M. Colby, K.J. Schultz, and J.N. Smith, et al. 2024. "Aryl hydrocarbon receptor-dependent toxicity by retene requires metabolic competence." Toxicological Sciences 202, no. 1:50-68. PNNL-SA-196662. doi:10.1093/toxsci/kfae098
  • Schultz K.J., J.H. Nguyen, V.S. Lin, C. Kombala Nanayakkara Thambiliya, K.J. Tyrrell, and P.M. Lalli. 2024. Heracles: Predictive Tools for Opioid Crisis Intervention - m/q Initiative Project Report. PNNL-36739. Richland, WA: Pacific Northwest National Laboratory. Heracles: Predictive Tools for Opioid Crisis Intervention - m/q Initiative Project Report
  • Thibert S.M., D.J. Reid, J.W. Wilson, R.A. Varikoti, N. Maltseva, K.J. Schultz, and A. Kruel, et al. 2024. "Native Mass Spectrometry Dissects the Structural Dynamics of an Allosteric Heterodimer of SARS-CoV-2 Nonstructural Proteins." Journal of the American Society for Mass Spectrometry 35, no. 5:912-921. PNNL-SA-189769. doi:10.1021/jasms.3c00453

2023

  • 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

2022

2021

  • 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