AI is strengthening the field of toxicology by improving drug safety and environmental health. Toxicologists and researchers use AI to develop advanced computational models that can quickly analyze and predict potential risks of various chemicals, drugs, and substances. Machine learning algorithms can be trained to recognize patterns in large datasets on chemical compounds and their toxicological effects.
In this activity, participants will explore the use of computational (in silico) methods as alternatives to animal testing. Through demonstrations of different software programs, they will learn how these methods can replace animal experiments, which are often painful and stressful for the animals. The aim is to show that computational approaches can partially substitute traditional testing, are ethically sound, and often more cost-effective. This activity is organized in partnership with the National Institute of Chemistry.
Organized by: Marija Sollner Dolenc in collaboration with colleagues from the National Institute of Chemistry (Faculty of Pharmacy, University of Ljubljana).