ChemogenomicsDB (CGDB) and Älixir are prime examples of creating data assets and insight solutions to help improve digital and data capabilities, enhance scientific productivity, and invigorate early pipeline. Using our products, Bayer R&D colleagues can - access to a broad range of data assets essential for solving key drug discovery questions - explore integrated biomedical and chemical data for scientific curiosity, inspiration, and knowledge discovery - generate insights with the assistance of interactive visualizations for evidence-informed decision making

Machine learning is a powerful tool that is increasingly being applied to a wide range of sectors and applications, from healthcare and medicine, to finance and marketing, to manufacturing and logistics. It is expected to drive productivity gains, enable innovation, and improve quality and efficiencies in many industries. We as a pharmaceutical company are constantly looking for new ways to improve our research and development pipeline so we can deliver innovative products and therapies that will provide patients with better treatment options. Machine learning offers many opportunities to improve processes and accelerate the discovery of new therapies, and we will continue to invest in this powerful tool to drive future advances in drug development. Our group, Machine Learning Research (MLR), has a proven track record in the field covering the areas of cell painting, large language modelling of proteins, and small molecule research. In this poster we give a broad overview over our projects and collaborations both internally and externally as well as showcasing our direct impact on the R&D pipeline. Artwork created using Stable Diffusion.

At the Fiesta de las funciones, we celebrated DS&AI's teams, sub-teams and projects in a dynamic, interactive poster session where everyone presented their work, and attendees could collect a POAP (Proof of Attendance Protocol) for each poster they visited, 20 posters and 21 POAPs. The POAPs served as a digital certificate of attendance and allowed attendees to gamify their participation in the event while getting to know a new digital technology. Utility was given to the person that collected the most POAPs, in the form of a prize given at the end of the event. Additionally, at the end of the poster session, there was a quiz that allowed attendees to test their knowledge of the presented posters. This quiz was a fun way to encourage attendees to interact with the posters and learn more about the various data science, ML and AI work currently going on in DS&AI. It was a great opportunity for Bayer DS&AI colleagues to interact and get to know each other and what we do, and to set the foundations for future collaborations.