As the newest member of the Applied Machine Learning team based in Boston Sara is excited to meet with you and brainstorm to define projects and form collaborations. She is a Bioinformatics and Computational Medicine researcher, expert in integration, analysis, visualization, and interpretation of (pre)clinical and high-throughput data. Her work involves developing algorithms to apply machine learning, heterogeneous data integration, and, 'omic' data and sizeable networks analysis to study complex genetic diseases (e.g., cancer and autoimmune disease), therapeutics response prediction, and computational functional genomics. Sara has over fifteen years of experience working and presenting in highly multidisciplinary teams of MDs, computational scientists, and biologists., and, writing articles and grant applications.

I am a "Senior Insight Solutions Lead" which means that I work in DAIS team and try to generate novel insights for our R&D (and beyond) organization

Thank you for the chat!

I am a pharmacist from Rio de Janeiro-Brazil and I work as a computational chemist at Bayer since 2021 in the Computational Molecular Design department in Wuppertal. I work side by side with chemists on different projects using our digital tools to design small molecules that maybe one day will help patients! I am passionate about my work and I have the opportunity to also be part of the Steering Group for Transformation and Leadership in the Drug Discovery Science to help transforming the way we work and communicate.

I studied piano performance and toxicology, and am now Master Data Manager at Bayer Pharma in the R&D Master Data Management team of Data Science & AI. I am passionate about team collaboration and rowing. Currently, I'm co-leading the IDMP Ontology, an international cross-industry project with 12 pharma companies for the unique identification of medicinal products to ensure patient safety. Talk to me about common standards and high quality data for better ML and AI outcomes, our next collaboration project, and/or our next jam session at the Bayer Berlin campus.

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.

Hey! I am a Research Engineer (Data Scientist) in DS&AI > Data Assets and Insights Solutions (DAIS) > Scientific Digital Solutions. I am responsible for implementing scientific solutions (namely: Aelixir, cellenium, ChemogenomicsDB, Bambus ...) and integrating data to make it easily accessibly and analyzable across the Bayer R&D Org. Feel free to reach out : dan.plischke@bayer.com My Professional Experience: Data Scientist: R&D Scientific Digital Solutions September 2020 - Present Corporate Student: Business Information Systems Oktober 2017 - Oktober 2020 My Academic Background: Master of Science - MS, Information Systems · (2020 - now) Humboldt University of Berlin Bachelor of Science - B.Sc., Business Information Systems · (2017 - 2020) The Berlin School of Economics and Law

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

Hubble is a pioneering data hub that continuously integrates and enriches terabytes of external scientific textual data to support insight generation along the R&D value chain. It opens opportunities for statistical analyses, large-scale text-mining and other data science methodologies to tease out important details and detect trends within e.g., patents, scientific literature, and grants. The Hubble platform is available Bayer-wide, and offers API access for data scientists. This allows large scale, programmatic analysis as well as integration into processes and platforms. Hubble is a cornerstone of our digital transformation journey within R&D. It serves many users, systems and processes with key data & analyses by answering ~2 million queries per week. We continue to extend the platform to best support insight generation and scientific decision making with a precision medicine focus. For more information contact: Astrid Rheinländer.

Team R&D Master Data Management (MDM) is responsible for the processes and technologies used to ensure the consistency, accuracy, and completeness of critical data that is used across various departments and functions within the R&D organization. MDM plays a crucial role in ensuring the reliability of scientific and clinical data used for research, drug development, regulatory submissions, and decision-making. Key responsibilities include: define and implement data standards & policies, develop and maintain a centralized master data repository, establish data quality controls, ensure data steward- and ownership, manage data integration from different sources and systems, ensure compliance with regulatory requirements and provide training and support for end-users across R&D to ensure proper use and interpretation of data.