Designing better drugs with machine learning

Developing safe and efficacious drugs is a multi-parameter optimization process. A good drug needs besides affinity to the receptor or protein target also good ADME (Absorption, Distribution, Metabolism, and Excretion) properties to be successful. Machine learning models are used to assess compound ideas early-on in the design process and give guidance what to synthesize next. We will present our scikit-learn based toolkit cream to build and validate predictive models. Results from validation studies using existing data as well as prospective prediction results will be shared. Special focus will be placed on how confidence in predictions can be increased using cross-validation procedures and by consideration of the domain of applicability of the predictions.