Nacho Molina is a professor of machine learning and data science in biotechnology at IE University, and Director of the Health and MedTech Lab within the IEX Research Xcelerator initiative. Before joining IE, he served as Research Director at the CNRS at the Institute of Genetics and Molecular and Cellular Biology in Strasbourg, where he led an interdisciplinary team at the intersection of biophysics, machine learning, and computational regulatory genomics. Professor Molina has held academic positions across Europe, including Adjunct Professor at the University of Strasbourg, Group Leader at the University of Edinburgh, and Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne. He earned his PhD in Computational Biology from the University of Basel, holds a Master’s degree in Fundamental Physics from the Complutense University of Madrid, and completed his master’s thesis in Quantum Field Theory at NIKHEF in the Netherlands.
Professor Molina’s research team develops interpretable, biophysics-informed deep learning models to understand how gene regulation drives cell proliferation, differentiation, and reprogramming in health and disease. By integrating spatial and single-cell multi-omics data, along with single-molecule molecular footprinting data, his group aims to decipher the molecular logic underlying dynamic cell cycle and cell fate decisions. Their computational frameworks, including DeepCycle, FateCompass, FourierCycle, and HiddenFoot, combine machine learning with mechanistic modeling to uncover how transcriptional and post-transcriptional regulation operate in developmental systems and cancer. His findings have been published in high-impact journals such as Nature Communications, PNAS, and Science. This research lays the groundwork for next-generation biomedical applications by integrating artificial intelligence with systems biology to develop predictive and mechanistic tools in regenerative medicine, precision oncology, and cell therapies.
Professor Molina has received several distinctions throughout his career, including the SIB Award for Best Young Bioinformatician, the Chancellor’s Fellowship at the University of Edinburgh, the USIAS Fellowship at the Institute for Advanced Study of the University of Strasbourg, and the Theory at EMBL Fellowship at the European Molecular Biology Laboratory. He was part of the coordination team of the European Innovative Training Network PEP-NET, which brought together international partners to connect epigenetics and mathematical modeling. His work has been consistently supported by competitive funding agencies, most recently through a grant from the Chan Zuckerberg Initiative to study cell cycle regulation in cancer using interpretable deep learning models.
