UL Cheminformatics Tool Kit

Industry is currently facing a substantial challenge. There is an increasing need to develop chemical hazard data for both new and existing chemicals, while at the same time there are current and proposed  regulatory mandates as well as ethical drivers to reduce, refine, and replace (the “3 R’s”) the use of animal test methods with alternative non-animal methodologies. This creates a challenge for innovators, formulators, and companies who strive to introduce a new chemical or fill data gaps on existing chemicals while adhering to the principles of the 3 R’s. In silico, or computational toxicology software offers an alternative method for predicting chemical hazards. Traditional Quantitative Structure Activity Relationship (QSAR) methodology employs simple analog identification and predicting hazard data from chemical analogs with known hazard data. This webinar will cover the UL Cheminformatics Tool Kit, a novel in silico approach combining big data with advanced machine learning incorporating data fusion. The approach integrates novel models called RASARs (Read-across Structure Activity Relationship) with data fusion techniques that extend this concept by considering all available property data on both the target and analogs rather than only the modeled hazard.