Daniel Probst

Dr. Daniel Probst is an Assistant Professor at Wageningen University & Research (WUR), where he leads research in machine learning applied to interdisciplinary problems at the intersection of chemistry and biology. His work focuses on scalable methods for data visualization and analysis, with contributions to drug discovery, molecular modeling, and cheminformatics. He holds a PhD in Chemistry and Molecular Sciences (2020) from the University of Bern, under Prof. Jean-Louis Reymond, and completed postdoctoral research with Prof. Pierre Vandergheynst at École Polytechnique Fédérale de Lausanne (EPFL), followed by a two-year tenure at IBM Research in biocatalysis. He is a recipient of the 2022 Sandmeyer Award for contributions to computational chemistry.

Education:

• BSc in Computer Science (2013), Bern University of Applied Sciences
• MSc in Bioinformatics & Computational Biology (2016), University of Bern
• PhD in Chemistry & Molecular Sciences (2020), University of Bern

Research Interests: Dr. Probst develops machine learning frameworks for molecular representation learning, generative modeling of protein conformations, and large-scale chemical space exploration. His methods address challenges in drug discovery, enzymatic reaction prediction, and sustainable biocatalysis. He emphasizes ethical AI practices and open science.

Awards:

• Sandmeyer Award 2022 (Recognized for contributions to digital chemistry via language models)

Teaching & Availability: Teaches courses on machine learning, data visualization, and bioinformatics research practice at WUR. Office hours are available daily at Radix Westvleugel (Wageningen), with contact details at Droevendaalsesteeg 1.

Labs & Teams: Heads the Probst Lab (probstlab.science), focused on computational tools for chemistry and biology.