jim-rathmans-group-modeling-complex-chemical-phenomena
About Us
Professor Rathman is an award-winning teacher who uses computational methods to predict chemical toxicity.
Before coming to Ohio State in 1991, he spent seven years in industrial R&D with Conoco Inc. and The Clorox Company. His current research efforts focus on computational toxicology, quantitative structure-activity relationship (QSAR) modeling, statistical data analysis, and computational risk assessment of complex chemical systems.
KEY DISTINCTIONS
- Co-founder, Altamira, LLC
NATIONAL AND INTERNATIONAL
Japan Research Institute of Material Technology:
- Lectureship Award, 2002
- Member, ACS and ASEE
REGIONAL
The Ohio State University:
- President and Provost’s Award for Distinguished Faculty Service, The Ohio State University, 2016
- Alumni Award for Distinguished Teaching, 1996. (Ohio State's highest teaching honor.)
- College of Engineering Charles E. MacQuigg Outstanding Teaching Award, 1995, 2001, 2005, 2011
- College of Engineering Lumley Award for Excellence in Research, 1997
Research
The ability to predict chemical toxicity is important for many reasons. Designing processes and products that have minimal negative impact on health and environment is essential in the development of pharmaceuticals, foods and food additives, consumer products, cosmetics, and specialty chemicals. It is simply not possible to experimentally study every new or proposed chemical compound to determine its toxicity potential. Also, recent initiatives and regulations that limit the use of animal testing for certain types of products (e.g., cosmetics) have motivated the development of alternative ways to evaluate toxicity, especially in vitro experimental approaches (as replacements for in vivo assays) and in silico computational approaches.
We are developing computational modeling methods to meet this need. We are attempting to model extremely complex phenomena; for example, will a new drug candidate react with DNA to cause mutations? Will a proposed new cosmetics ingredient possibly be a skin sensitizer, eliciting an allergic response in some people? To answer such questions we combine cheminformatics methods with advanced computational modeling techniques, generally referred to as “machine learning”, to build predictive models. Our models are trained, tested and validated using the extensive and diverse databases available that summarize chemical property and biological endpoint data for many thousands of molecules for which experimental data from some of these endpoints are available.
