Lead development and performed business analysis for a global registration system used for storing, annotating and querying the metadata for all sequence-based biological reagents at NIBR including cDNA and shRNA plasmids, antibodies, peptides and aptamers. The system leveraged bioinformatics tools to automate and simplify much of the registration process.

My responsibilities included implementing and optimizing various cheminformatics tools and algorithms in Python, C, PipelinePilot, and other platforms with the goal of better supporting HTS and Lead Discovery at NIBR. I was also involved in developing a novel ligand-based in-silico screening and target prediction method which utilized legacy in-house high-throughput screening data to make predictions.

Performed computational modeling including conformational analysis and SAR studies of food derived peptide fragments. Used structural analysis and homology models of opioid receptors to predict the bioactive conformation and binding position of the peptides and explain activity differences between different isoforms. The predictions were also validated with wet-lab experiments.

Developed software to support the capture and analysis of data from in vivo oncology studies. Presented a poster on the use of XML as a data storage and transfer language for life science informatics at an internal drug discovery seminar in Basel, Switzerland.