Cheminformatician @ Machine Learning Research

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

I've met Stefanie Schmittel during a DS&AI meeting in May 2023 in Madrid. She works within Data Strategy and Governance in the group Proprietary Information Management.

I'm the Chief of staff for DS&AI. I'm the go-to-person for:​ Evolution of our strategy and culture​ Connecting the dots within and across the organization​ Topics you want to present to/discuss with the LT​

Wen Zhang Base: Chengdu, China Working experience in Bayer: Apr 2023: Data Science Services & Solutions Oct 2019-Mar 2023: Medical Writing Digital Innovation Apr 2019-Sep 2019: Medical Writing Operations

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.