Francesca Grisoni is an Assistant Professor at the Eindhoven University of Technology, where she leads the Molecular Machine Learning team. After receiving her Ph.D. in 2016 at the University of Milano-Bicocca (Prof. R. Todeschini) with a dissertation on machine learning for (eco)toxicology, Francesca worked as a data scientist and as a biostatistical consultant for the pharmaceutical industry. Later, she joined the University of Milano-Bicocca and the ETH Zurich as a postdoctoral researcher (Prof. G. Schneider), working on machine learning for drug discovery and de novo design. Her team’s research focuses on developing novel chemistry-centred deep learning methods to augment human intelligence in drug discovery, at the interface between computation and wet-lab experiments.

Derek van Tilborg is a Ph.D. candidate in the Molecular Machine Learning team. Derek holds a MSc degree in bioinformatics from Wageningen University & Research and has a background in biomedical research. He currently works on developing machine learning methods for molecular property prediction, aiming to bridge the gap between predictions and experiments in drug discovery. His research interests are focused on graph neural networks and active learning.

Rıza Özçelik is a Ph.D. candidate in the team. He received his MSc. degree from the Department of Computer Engineering at Boğaziçi University, Turkey, where he applied machine learning to structure-based drug-target affinity prediction. Rıza currently focuses on developing novel generative deep-learning approaches for de novo drug design.

Luke Rossen is a prospective PhD Candidate in the Molecular Machine Learning team (starting in 2024). Previously, he joined the group as a Master student at TU/e in the Biomedical Engineering department – Chemical Biology cluster, where he managed and participated in the ‘international genetically engineered machine’ (iGEM) Competition team. His research interests are reflected in a recently completed thesis on active learning for drug discovering using graph neural networks, working at the interface between the wet lab and computational work to efficiently navigate the chemical space. Luke has pursued an internship at the Novartis Institute for Biomedical Research, Switzerland, working on generative deep learning models for molecular (scaffold) design.

Helena Brinkmann is a Ph.D. candidate at the Molecular Machine Learning Team. She completed her undergraduate studies in the subjects Mathematics and Chemistry at the Eberhard Karls University Tübingen in Germany. After working as a teacher for two years, she received her MSc degree in Medicinal Chemistry from the University of Gothenburg, where she first applied machine learning for drug discovery. Currently, she focuses on the encoding of chemical information to augment the capabilities of de novo molecule design and chemical space exploration.

Meilina Reksoprodjo is a Master Student in Data Science at the Eindhoven University of Technology. Her research interests encompass a diverse range of topics in machine learning, with a particular focus on AI for accelerated scientific discovery and geometric deep learning. In 2021, she obtained her BSc in Artificial Intelligence from Radboud University where she conducted research on designing value-sensitive, data-driven healthcare systems. At the time of writing, she is finishing her degree by writing her thesis in the field of structure-based drug design, where she is developing a graph-based generative model for the hit-to-lead optimization step of small therapeutic molecules. Outside of her academic pursuits, she finds joy in great puns and travelling.