Bethany Cook

Bethany Cook is a computational chemist with over 10 years of experience, specializing in structure-based chemical workflows, computational toxicology, and reproducible workflows. She develops chemical-based workflows using tools like Python and KNIME to utilize structure-based information to enhance QSAR (Quantitative Structure-Activity Relationship) workflows. Bethany has extensive experience in predictive high-throughput screening, large-scale data mining, and toxicological analysis. At RTI International, she develops workflows that streamline the analysis of large chemical datasets, enabling more efficient prioritization of chemicals for regulatory testing. Her pipelines enhance the accuracy of QSAR models while providing a reproducible, transparent system for evaluating chemical hazards, directly supporting regulatory decisions. Notable projects include supporting the U.S. Air Force in chemical exposure hazard assessments, contributing to the EPA's Endocrine Disruptor Screening Program, curating data models to ensure data accuracy and consistency for RMIP, and working to eliminate animal testing by developing New Approach Methodology (NAMs).

As a computational chemist, Bethany provides technical expertise in understanding chemical and biological interactions for risk assessments of health and environmental impact. She has played a key role in growing QSAR capacities and contributed to projects aimed at improving risk assessment tools and techniques. Bethany has supported both commercial clients and government contracts. 

Bethany Cook holds a Master of Science in Chemistry with a concentration in Computational Chemistry and Cheminformatics from North Carolina State University. Her graduate research focused on cheminformatics modeling and QSAR models, where she applied computational methods to analyze drug candidates and molecular interactions.