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

Great meeting you!!! I am domain SME in Clinical and Healthcare systems.

Position: Sr. Manager, Value & Insights Team: Value, Engagement & Training (VET) Dep.: Product Owner Hub (POH) Location: Wuppertal, Germany

Over the past two decades, the interdisciplinary predicTeam has established a prediction platform at Bayer Pharma R&D with the goal to generate state-of-the-art machine learning models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. The predicTeam provides an all-inclusive package covering the data pipeline from experiment to application in projects. In close interaction with experimentalists, we select endpoints for model building that are relevant for drug discovery. We implement and maintain the infrastructure to retrieve and prepare the data and make it accessible as a data lake. For each endpoint, after fully exploring the matrix of data, molecule representations and algorithms, we implement the best-performing and most stable-models models in our internal research platform Pix. A highly automated infrastructure allows us to perform weekly retraining of the models to ensure that the novel chemical space of drug discovery projects is well embedded. We ensure close interaction (e.g. presentations, tutorials, teams channel) with the user base for optimal model use and direct feedback allowing for constant improvements. Finally, our Model Performance Report helps users to assess the applicability of each model to their specific project molecules.

Understanding how compounds affect the disease biology of patients, and which potential side effects could be expected, is one of the core challenges in drug discovery. Transcriptomics (RNA-seq) allows us to measure these compound effects via an unbiased readout of gene expression changes. While „conventional“ analysis of RNA-seq data yields insights into individual compound effects and similarities, deeper connections between principles of compound structure and transcriptome effect elude us. Insights into this connection would help us support project teams with data-driven decision making on compounds. In this pilot, we are bringing together a cross-functional team of colleagues from LST, CMD, SyMOL and DS&AI to tackle this challenge and build a toolkit to enable understanding of ​how compound structure relates to complex gene expression patterns​ and to explore common themes amongst structures or shared off-target/general perturbation effects. This toolbox will become the starting point for other efforts aimed at leveraging multi-omics data in understanding compound mechanism of actions. Artwork created using Stable Diffusion.

The R&D Digital Fluency Program (DFP) is supporting you with learning offerings to build up competencies and further skillsets around digital technologies to enable a competitive and digitally literate organization. At the Digital Fluency Program, we set ourselves apart by offering learning opportunities created by your peers who understand the unique challenges you face in your job. Our tailored approach focuses on real-life use cases, taking our training far from generic and providing you relevant and impactful skill development. In 2020 we, a small team of four people, started our journey with the DFP as part of the R&D Digital Roadmap. Since technologies are ever evolving and impacting our work environment it is more important than ever to foster a culture of continuous learning and enable future opportunities. Thus the DFP learning portfolio includes something for everyone, regardless of your level of knowledge: It offers general awareness programs to enhance basic digital literacy, self-learning pathways on data analysis & visualization and the ability to access online courses like Coursera via the Digital Curriculum. The team ensured the learning objective was met by piloting newly developed learning programs, making use of cross-functional development, and incorporated R&D specific components prior to a broader roll-out in the organization.