Rasmus Mejborg Borup

Rasmus Mejborg Borup is a Guest Researcher in the Department of Chemistry at the Faculty of Science, University of Copenhagen, with research focusing on computational chemistry and machine learning applications for predicting chemical properties. His work bridges quantum chemistry with data-driven approaches to solve complex problems in organic reactivity.

Currently pursuing his Ph.D. at the University of Copenhagen, Borup's dissertation research centers on C-H reactivity prediction through integrated quantum chemistry and machine learning frameworks. His academic trajectory demonstrates strong specialization in computational methodologies for chemical discovery.

His research interests emphasize computational organic chemistry, with specific expertise in pKa prediction, C-H bond reactivity modeling, and cheminformatics tool development. Borup actively contributes to advancing predictive algorithms that accelerate reaction discovery and molecular design, particularly through the integration of quantum mechanical calculations with artificial intelligence techniques.

Recent publications reveal a clear trajectory in developing practical computational tools for organic chemistry, with his 2024 pKalculator software establishing foundational methods for C-H bond acidity prediction, now being extended to full reactivity modeling in his doctoral work. This progression highlights growing sophistication in merging quantum chemistry with machine learning for chemical applications.

No scientific awards are currently documented in available sources.

Borup operates within the research ecosystem of the University of Copenhagen's Department of Chemistry, where his work on computational prediction tools indicates active collaboration with quantum chemistry and organic synthesis research groups. His projects likely receive institutional support through standard doctoral funding mechanisms at the university.

As a core member of computational chemistry research teams at the University of Copenhagen, Borup contributes to advancing cheminformatics methodologies through software development and algorithmic innovation, with particular focus on making quantum chemistry predictions more accessible for synthetic chemistry applications.