Francesca Grisoni is an Associate 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. Prof. Grisoni has received several grants and awards, such as the FEBS Excellence Award, the Early Career Award from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW), an NWO Vidi and NWO XL, and an ERC Starting Grant.

Sofia Imperatore is a mathematician by training with a BSc (2017), MSc (2020), and PhD (2024) in Applied Mathematics from the University of Florence, Italy. During her PhD, she focused on geometric deep learning, combining free-form geometric models with data-driven systems, as well as deep-geometric generative models. After her PhD, she undertook a researcher position at CNR in Pavia, Italy, working on uncertainty quantification via sparse grid surrogate models. In her current role as a Postdoctoral Research Fellow in the Molecular Machine Learning group, she focuses on developing deep protein representations enriched with geometric information for drug discovery.

Sébastien Sueron is a Postdoctoral Research Fellow in the Molecular Machine Learning group. He holds an MSc in Chemistry from the University of Montpellier, with a background in organic and analytical chemistry. He obtained his PhD in Organic and Medicinal Chemistry in 2024 from the Faculty of Pharmacy at the University of Rennes. His doctoral work focused on hit-to-lead optimisation of a kinase inhibitor showing promising activity in a murine in vivo model, alongside an induced degradation approach on the same therapeutic target. In late 2023, he spent a few months at the University of Gothenburg where he discovered computational chemistry and developed a pipeline to accelerate the identification of functional PROTAC degraders. With an experimental background, he joined the MolML group in 2025 to further explore the computational side of chemistry through AI-driven generative models for drug discovery.

Elena Frasnetti studied Chemistry at the University of Insubria and the University of Pavia. During her master’s studies, she discovered her passion for computational biochemistry and drug discovery, which led her to pursue a Ph.D. in the same field. The main topic of her doctoral thesis focused on integrating Physics-based methods with Machine Learning algorithms for drug design. During her Ph.D., she spent four months as a guest at the Molecular Machine Learning team, where she worked on the use of Chemical Language Models for the development of allosteric inhibitors. This period sparked her interest in Cheminformatics, leading her to return to the group as a Postdoctoral Fellow, working primarily on active learning for drug discovery.

Barbara Terlouw is a postdoctoral researcher in Bioinformatics at Wageningen University & Research and at the Eindhoven University of Technology. Her research focuses on structural bioinformatics, machine learning, deep learning, non-ribosomal peptide synthetases, and biosynthetic gene clusters. She completed her PhD at Wageningen University in 2023. She has contributed to several high-profile bioinformatics projects, including the antiSMASH pipeline for predicting biosynthetic gene clusters and the MIBiG database for annotating experimentally validated gene clusters.

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 PhD candidate in the Molecular Machine Learning group. She completed both her bachelor and master degrees in Biomedical Engineering in the group, focusing on structured state space models for de novo drug design and molecular task arithmetic to edit chemical language models, respectively. Prior to her doctoral studies, she completed an internship at IBM Research in Zurich as part of the AI for Scientific Discovery team. During her PhD, Sarah will work on infusing chemical knowledge and intuition into deep learning models to advance drug design methodologies.

Markel Benito Sendin is a PhD candidate in the Molecular Machine Learning team. Originally from the Basque Country in northern Spain, he obtained a BSc in Biochemistry from the University of Navarra (Spain) and an MSc in Bioinformatics from Utrecht University (the Netherlands). His research focuses on developing a ML–guided framework for the de novo design of hybrid macrocyclic peptides with therapeutic potential, combining deep learning and directed evolution to accelerate drug discovery.

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.

Lucas van Osenbruggen is a Master’s student from Data Science & Artificial Intelligence and Computer Science & Engineering. He is currently researching biosafety of protein language models to help ensure that AI helps human health instead of harming it. Next to his studies he has his own company in business process automation. With the time that remains he likes playing volleyball and scuba diving.

Arthur Monnier is a Master’s student in Artificial Intelligence and Engineering Systems at TU/e. He is particularly enthusiastic about applying machine learning methods to critical challenges in scientific discovery and drug design. His research focuses on improving the selectivity of generative models for ligand design. Outside of his research, Arthur finds joy in playing the guitar and tennis.

Bram Boerenkamp is a Master’s student in the Biomedical Engineering Department at TU/e, driven by an ambitious vision of a world where every disease has a viable therapeutic solution. Recognizing the potential of artificial intelligence to accelerate drug discovery, he joined our team during his bachelor in biomedical engineering to pursue this goal through computational means. His research centers on the de novo generation of molecular glues designed to address a therapeutically relevant challenge aligned with his broader interests. When not engaged in research, he channels his energy into cycling and mountainbiking.