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.

Emanuele Criscuolo studied Chemistry at Sapienza University and Tor Vergata University, in Rome. During his master studies he spent about one year at IRBM Science Park, focusing his work on fragment-based drug discovery. In February 2023, He obtained his Ph.D. cum laude in Biochemistry and Molecular Biology, working in the Department of experimental medicine at the University of Rome Tor Vergata. During his doctoral studies, he discovered his passion for computational techniques, combining them with experimental procedures. In 2021, He has received a short-term FEBS fellowship and he was hosted at Leiden University. One year later, he joined Grisoni’s group in Eindhoven, as guest PhD student, to combine his passion for Molecular Dynamics with Machine Learning. In 2023, he joined the team as a Postdoctoral fellow. 

Andrea Gardin is a Postdoctoral Research Fellow in the Molecular Machine Learning group. He completed his MSc. in Chemistry at the University of Padua, where he focused on computational methods to study complex molecular systems. In October 2023, he earned his Ph.D. in Material Science and Technology from the Polytechnic University of Turin. During his Ph.D., Andrea primarily worked on techniques for detecting and classifying the structural features of various supramolecular materials. Since joining this research group in January 2024, he has been applying machine learning to study and optimize a wide range of supramolecular materials, with a particular focus on, but not limited to, coacervates.

Luke Rossen is a PhD Candidate in the Molecular Machine Learning team. 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.

Sarah de Ruiter is a Master Student of Biomedical Engineering. She completed her Bachelor thesis on “Structured State Space Models for de novo drug design” in 2023, for which she won the Best Thesis Award of the Biomedical Engineering Department. Sarah returned to the Molecular ML group for her Master thesis in 2024. Her current research consists of applying model editing techniques to deep learning models for drug discovery.

Inge Groffen is a Master student at the TU/e pursuing Data Science and Artificial Intelligence. Prior to this, she completed a joint Bachelor’s degree in Data Science at the TU/e and Tilburg University. She has always had a strong interest in biology, and her current thesis allows to apply her knowledge in a biomedical context. Specifically, her research involves generating new molecules using target-based generative models. Aside from her studies, she enjoys playing tennis, practicing the piano, and traveling.

Ben Adams is a Master Student in the Molecular Machine Learning team.