Pre-clinical drug testing and clinical data analysis along with protein docking experiments have led to my gaining extensive knowledge of the process, procedures, and problems associated with testing new compounds.

I worked heavily in a quality pedigree in pharmacology, drug discovery, docking, and machine learning for pre-clinical and clinical drugs, including assisting with reuse of compounds for the COVID-19 pandemic. From in silico docking design to pre-clinical assay design and decision making of moving forward with clinical trials, I can help your company/lab get and stay on a solid path.

  • AI and machine learning techniques and algorithms in Python, including mathematical and statistical bases, deep learning, neural nets, and PyTorch techniques
  • Chemical fingerprinting machine learning techniques for drug structure grouping, promiscuity, function, and more
  • BIOVIA Pipeline Pilot modular programming expertise for ETL, machine learning, and data analysis
  • COVID-19 centric data analysis to quickly and efficiently find and score potentially effective compounds based on internal proprietary assays and analytical pipelines.
  • Project manager for data curation contract of small molecule indications, side effects, targets, identifiers, interactions, and more
  • Research project to effectively integrate and uniquely analyze data from Tox21 project reporter gene assays through chemical structure machine learning, deep metadata annotation analysis, and cross-dataset virtual screening
  • Research project performing high-content screening (HCS) on stem-cell derived human motor neurons examining effect of kinase inhibition on neurite outgrowth


  • Cooper, D. J., & Schürer, S. (2019). Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures. Molecules, 24(8), 1604. Retrieved from