The assembly of β-barrel membrane proteins by BAM and SAM
Recommended citation: Y Lundquist, K, Billings, E, Bi, M, Wellnitz, J, Noinaj, N. The assembly of β-barrel membrane proteins by BAM and SAM. Mol Microbiol. 2021; 115: 425–435.
CV
Education
- B.S. in Biology & Data Science, Purdue University, 2021
- Ph.D in Pharmaceutical Sciences, University of North Carolina Chapel Hill, 2026 (expected)
Experience
- June 2023 — Current: Intern in Computational Drug Discovery
- Orogen Therapeutics
- Full-Time (Summer only), Part-Time
- Worked on various computational and machine learning project to accelerate early stage discovery
- January 2023 — June 2023: Consultant in Computational Drug Discovery
- Animol Discovery
- Part-Time
- Worked on various computational and machine learning project to accelerate early stage discovery
- Fall 2021 — Current: Graduate Research Assistant
- University of North Carolina Chapel Hill
- Co-Advisors: Alexander Tropsha & Konstantin Popov
- Work on research projects in the area of DNA encoded library machine learning and computational early stage drug discovery screening
Skills
- Python
- Numpy
- Scikit-Learn
- PyTorch
- RDKit
- Large scale virtual screening
- Schrodinger Suite
- Molecular Docking
- Molecular Dynamics
- Quantitative Structure Activity Relationship (QSAR) Modeling
Publications
The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation
Travis Maxfield, Joshua Hochuli, James Wellnitz, Cleber Melo-Filho, Konstantin I. Popov, Eugene Muratov, & Alex Tropsha. (2023). The N-ary in the Coal Mine: Avoiding Mixture Model Failure with Proper Validation.
Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds
Alves, V. M., Yasgar, A., Wellnitz, J., Rai, G., Rath, M., Braga, R. C., … Tropsha, A. (2023). Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. Journal of Medicinal Chemistry, 66(18), 12828–12839.
Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries
Wellnitz, J., Popov, K.I., Maxfield, T, and Tropsha, A. (2023), Hit Discovery using Docking ENriched by GEnerative Modeling (HIDDEN GEM): A Novel Computational Workflow for Accelerated Virtual Screening of Ultra-large Chemical Libraries.. Mol. Inf.. Accepted Author Manuscript.
Talks
2023 — Oral presentation, Southeastern Regional Meeting of American Chemical Society (SERMACS), Medical Chemistry division (MEDI)