The goal of this 2014 challenge series was to develop a model, based on data provided by the U.S. Environmental Protection Agency, to quantitatively predict a chemical’s systemic Lowest Effect Level (LEL) in a traditional animal toxicity study. The systemic LEL is the lowest dose that shows adverse effects in these animal toxicity tests. The LEL is then conservatively adjusted in different ways by regulators to derive a value that can be used by EPA to set exposure limits that are expected to be tolerated by the majority of the population.
Entrants to this challenge had to develop models using data from high-throughput in vitro assays, chemical properties, and chemical structural descriptors to quantitatively predict a chemical’s systemic LEL (see the ideation challenge; algorithm development problem statement). 49 competitors submitted 804 solutions for a $10,000 prize. The result yielded an improvement by 20% in the accuracy of prediction above the current state of art. For more information, please visit the here or here.