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

Joshi, R. P., K. J. Schultz, J. W. Wilson, A. Kruel, R. A. Varikoti, C. J. Kombala, D. W. Kneller, S. Galanie, G. Phillips, Q. Zhang, L. Coates, J. Parvathareddy, S. Surendranathan, Y. Kong, A. Clyde, A. Ramanathan, C. B. Jonsson, K. R. Brandvold, M. Zhou, M. S. Head, A. Kovalevsky, and N. Kumar. 2023. “Ai-Accelerated Design of Targeted Covalent Inhibitors for Sars-Cov-2.” Journal of Chemical Information and Modeling 63 (5): 1438-1453. https://doi.org/10.1021/acs.jcim.2c01377.

Schultz, K. J., S. M. Colby, Y. Yesiltepe, J. R. Nuñez, 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 (2): 1197-1214. https://doi.org/10.1039/D0CP03620J.

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 Α2a Adrenoceptor Agonists.” Journal of Chemical Information and Modeling 61 (1): 481-492. https://doi.org/10.1021/acs.jcim.0c01019.