Bayer AG is global enterprise with core competencies in the Life Science fields of healthcare (pharmaceuticals and consumer health) and agriculture. Its products and services are designed to benefit people and improve their quality of life. Bayer is committed to the principles of sustainable development and to its social and ethical responsibilities as a corporate citizen. In fiscal 2021, the Group employed around 99,600 people and had sales of EUR 44.1 billion. Bayer has established the Machine Learning Research group to develop the tools needed to impact drug discovery in the age of digitalization. Molecular embeddings are developed using input sequences, graphs and 3D shapes of molecules. Such representations are used downstream to optimise drug candidates, predict their ADMETox properties, or prioritise synthesis to name a few. Models are based on high-quality proprietary data and put in the hands of the end users via our deployment platform. Explainability has been in the spotlight recently, with new methods to improve the explainability of graph neural networks, explain QSAR model outcomes (in prep.), or make sense of unsupervised embeddings. The Fellows will learn how to apply and develop explainability methods in relevant use cases for the pharmaceutical and agrochemical industry.