In silico modeling using in vitro high throughput screening data for toxicity prediction within REACH
Sayed A. Ahmed, Ahmed Mohamed Abdelaziz · 2016
Quantitative structure activity relationships and advanced machine learning algorithms were successfully utilized to develop models for predicting toxicity according to OECD principles. The models use data from high throughput screening and in silico descriptors to provide an alternative approach for filing information gaps within the REACH regulations. Developed models for multiple nuclear receptors, stress response pathways as well as animal toxicity endpoints are made publicly available.