Byron appreciation token 2023: A show of gratitude for your usage of the Bayer platform in 2023.
2023-12-22
Cheers to our global gang! 🌍🌟 As the year draws to a close, we're giving a shoutout to the 13,000 amazing contributors on our platform. By leveraging Byron, the exchange within our company becomes more seamless, paving the way for new projects and strengthening our unity within Bayer. 🚀 Presenting this exclusive POAP as a massive thank you, because without you, this platform wouldn't be what it is. 🙌 Byron, an acronym for Bayer Open Network, is our Skills database for Bayer employees, including speed networking. We kicked off in 2018 as a True North grassroots side initiative, going live in 2020. Created by over 40 students and programming enthusiasts with the help of EY Croatia. Today, we have 13,000 registered users from 89 countries, in 293 cities. People have searched for something in Byron at least 25,000 times and engaged in 31,000 1:1 Speed network meetings. http://go/byron Looking ahead, we're thrilled about the future and the prospect of our community growing even larger. Can't wait for more collaborations and successes together in the coming year! 🎉✨ Art created by Lena Schermer
The Applied Machine Learning Group (AML) strives for the application of a data-driven approach to drug research and development by developing and utilizing best-in-class machine learning (ML) models to optimize R&D processes. The implementation of ML algorithms accelerates drug discovery and a platform to monitor and self-train models enables portfolio view of our capabilities, allowing us to become more focused. We envision to work in a fast-paced setting, we expect to fail quickly and adapt swiftly, all while expanding your ML capabilities. We also believe in making the right choices not only for our immediate team but decisions to build solutions catered to broader R&D. The success at AML will be measured by bucketing the benefit of the developed capability into one or more of these three buckets, (1) Savings in time (2) Savings in cost (3) Generation of new insights
Morphological profiling with Cell Painting allows an unbiased characterization of cellular states by observing changes in cell morphology. Cell Painting images are information-rich and can guide the elucidation of mode of action, toxicity, and off-target effects, help drug repurposing, indicate differentiation states of iPSC lines, discover new biology and more. However, this technology is data-greedy and computationally complex and demanding. At DS&AI, we enable this technology at Bayer! To this end, we support large data generation campaigns internally and externally, develop novel machine learning algorithms, and create tools for efficient analysis and visualization of Cell Painting data. For more information please contact Paula Marin Zapata or Marc Osterland. Self-painted image modified using pictures from https://quizlet.com/420027050/animal-cell-diagram/ as starting models.
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 Language of Life, LoL, we combine state-of-the-art machine learning models, bioinformatics and expert knowledge to understand and design large biomolecules (RNA, DNA, Proteins), helping deliver the best drug, faster and cheaper, to address our patients’ needs. Our objectives are, 1) Drug the undruggable 2) Target any modality 3) Efficient Dry lab – Wet lab loops 4) Understand Patient & Disease
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.
With the new head of R&D Community Engagement, RDCE, a new set-up has also been introduced, which newly regulates the tasks and responsibilities of the business partners assigned to the functions, SPOCs, which stands for Single Point of Contact. What is now in scope and what is not? The poster provides information on this.
Collaborations with external partners may lead to joint legal rights for compounds and data, which may restrict reusage in other projects. With the increasing number of collaborations at Bayer and with advancing time, memories of the project details fade, and reconstruction of the status will be nearly impossible. Therefore, we aim for a system to document the role of compounds, which will facilitate decisions on further usage in other collaborations. For more information contact: Miriam Wollenhaupt. Images taken from Microsoft Office free for use pictograms.
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.