Staff information

PNNL Publications

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

• Kim H., D.G. Hauner, J.A. Laureanti, A. Kruel, S. Raugei, and N. Kumar. 2022. "Mechanistic Investigation of SARS-CoV-2 Main Protease to Accelerate Design of Covalent Inhibitors." Scientific Reports 12, no. 1:Art. No. 21037. PNNL-SA-170467. doi:10.1038/s41598-022-23570-6
• 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