The Bayer Pharma Data Science & Artificial Intelligence (DS&AI) F2F meeting in Madrid was a great event! The motto Connect, Clarify & Commit came to life as we connected during networking sessions, breakout sessions and social events. At the poster session we learned about our progress embedding a product mindset and driving operational excellence, closely collaborating with our partners across R&D. We heard presentations, took part in discussions, unconference sessions, asked questions and shared ideas, setting foundations for the growth of DS&AI beyond 2023. The use of POAPs was a definite social success and I am sure you did not miss Jesús’ enthusiasm as he gave an overview of this new tool. By promoting its use, we drive new ways of using this sustainable and effective tool, ultimately helping to innovate its use by doctors, within clinical trials, for patients and more. There were fun elements as well, like the final night, where our very own flash-mob took to the dance floor and we danced until way past midnight! All this was made possible by the org team: Anke Ebert, Larsen Schnadhorst, Kathleen Thies, Sheila Elz, Stefanie Holt-Noreiks and Claudia Vogt. They have done an excellent job coordinating the event, with the perfect balance of presentations, speakers, entertainment, and networking. Now let us make sure we continue to further the impact we have on bringing innovative medicines to our patients. Sai Jasti, Head of DS&AI https://bayer.com/poap

Sai works at Bayer Pharmaceuticals R&D as SVP, Head of Data Science & Artificial Intelligence, DS&AI. His previous experience includes working at GSK, AstraZeneca and Deloitte. Please contact him for any question you may have or just want to engage on any fun topic.

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.