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Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +7 more

Rasmus Mikelsons serves as a Lecturer at the Department of Computer Science , University of Copenhagen . His email contact is rami@di.ku.dk . He is affiliated with the Machine Learning section and participates in the SCIENCE AI Centre, focusing on both theoretical and applied machine learning research. Research Interests Machine Learning Natural Language Processing Medical Data Analysis Remote Sensing Sustainability Computational Biology The Machine Learning section conducts research ranging from theoretical foundations to applications in domains such as information retrieval, medical imaging, and biological data modeling. Recent publications highlight trends in quantum machine learning, sustainable AI, and multimodal applications. Labs and Teams He is part of the Machine Learning section at the Department of Computer Science, which has access to a dedicated compute cluster and participates in initiatives like TreeSense, a center for remote sensing and deep learning of global tree resources.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +4 more

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.

• Computational Chemistry
• Organic Chemistry
• Machine Learning
• +3 more

Albert J. Kooistra is an Associate Professor in Data Science for Drug Design (DSDD) at the Department of Drug Design and Pharmacology, University of Copenhagen (DK). He heads the Pharmaceutical Data Science team and the Data and Computing Facility, where he develops computational approaches to advance pharmaceutical research and drug discovery. Dr. Kooistra earned his Master's degree in bioinformatics followed by a PhD in computational medicinal chemistry from the Vrije Universiteit Amsterdam (NL), with postdoctoral training in molecular dynamics, structural bioinformatics, and cheminformatics. His research spans structural bioinformatics, cheminformatics, computer-aided drug discovery & design, research tool/database development, and machine learning/AI applications in pharmaceutical sciences. He is the founder of KLIFS (https://klifs.net), a structural kinase database used by over 1,600 researchers monthly, and has made significant contributions to GPCRdb. His work bridges computational science with pharmaceutical applications to accelerate therapeutic development. Dr. Kooistra's recent publications demonstrate his focus on structural databases for drug discovery, particularly in kinase and GPCR research. His work integrates AlphaFold2 predictions, database development, and structural analysis to create valuable resources for the scientific community. MGMS Frank Blaney Award (2021) from Molecular Graphics and Modelling Society for enabling research via scientific databases Educational Innovation Award (2017) from VU University Amsterdam for implementing Virtual Reality in pharmaceutical education Dr. Kooistra has developed innovative educational programs across multiple levels, including AI in Drug Discovery (international PhD course), Hands-on Computational Medicinal Chemistry, and Digital Core Curriculum implementation for undergraduate data science education. His teaching reaches hundreds of students annually, from high school to PhD level. He leads the Pharmaceutical Data Science team and the Data and Computing Facility at the University of Copenhagen, providing essential computational infrastructure and expertise for pharmaceutical research while maintaining active industry collaborations through Synamics Therapeutics (Co-founder/CTO) and Kvantify (Senior Scientific Expert).

• Structural Bioinformatics
• Cheminformatics
• Computer-Aided Drug Discovery
• +6 more

Casper Steinmann is an Associate Professor in the Department of Chemistry and Bioscience at Aalborg University's Faculty of Engineering and Science. His research is centered on computational and applied supramolecular chemistry, with a focus on molecular modeling, drug design, and chemical informatics. He leads several research projects integrating machine learning and robotics in chemical synthesis. His primary research interests include computational chemistry, supramolecular interactions, cyclodextrin-based systems, docking simulations, and the development of interoperable cheminformatics tools. He is particularly active in structure-guided drug discovery and the prediction of physico-chemical properties of complex molecular systems. The recent publications highlight a strong trend in computational drug discovery, targeting viral proteins like SARS-CoV-2 helicase and neurodegenerative disease targets such as LRRK2. His work also spans materials science, particularly in nanoparticle-based antimicrobial therapies, and software development for computational chemistry, exemplified by the cclib 2.0 project. These reflect interdisciplinary research bridging chemistry, biology, and computer science. Årets Underviser på Institut for Kemi og Biovidenskab (2022) Årets Underviser på Ingeniør- og Naturvidenskabelige Fakultet (2022) Casper Steinmann has been actively involved in research supervision and external examination of PhD theses, indicating a strong commitment to academic mentoring. He has secured multiple research grants, including funding from the Villum Foundation and Danish e-infrastructure Cooperation, supporting projects in automated synthesis, machine learning in chemistry, and molecular property prediction. His activities include extensive peer review, conference participation, and industry talks. He is involved in projects such as 'Designing Molecules for Ease of Chemical Synthesis' and 'Prediction of physico-chemical properties of guest cyclodextrin inclusion complexes,' often in collaborative networks. His team engages with robotics in chemical synthesis and develops computational frameworks for polarizable embedding and molecular simulations.

• Computational Chemistry
• Supramolecular Chemistry
• Molecular Modeling
• +7 more

Anna Duncan is a tenure track assistant professor in the Department of Chemistry at Aarhus University's Faculty of Natural Sciences. Her research focuses on computer modeling of biological membranes and membrane proteins to understand their functional dynamics. She began her position in 2022 and is currently establishing her independent research group after transitioning from a postdoctoral role. Undergraduate Degree in Mathematics – University of Oxford Master's Degree in Cheminformatics – University of York PhD – University of Cambridge Postdoctoral Research – University of Oxford Anna's research lies at the intersection of chemistry, biology, and computational science. She uses advanced simulation techniques to study how lipids and proteins interact within biological membranes. Her interdisciplinary background enables her to approach complex biophysical questions with mathematical rigor and data-driven methods. She emphasizes curiosity-driven research and aims to contribute knowledge for the public good. Although no publications are listed in the provided text, her work is expected to fall within computational biophysics, focusing on membrane organization, protein-lipid interactions, and dynamic cellular processes. Future outputs will likely involve molecular dynamics simulations and multiscale modeling approaches. Anna values mentorship, collaboration, and work-life balance in academia. She encourages early-career researchers to follow their interests, embrace new opportunities, and seek supportive mentors. While no formal awards or grants are mentioned, her training at leading institutions and successful transition to a tenure-track role reflect strong recognition of her potential. She is currently on parental leave but remains actively engaged in building her research team. The Department of Chemistry at Aarhus University has provided significant logistical and professional support during her relocation from England, helping her integrate into both the academic and personal environment in Denmark.

• Computational Chemistry
• Biological Membranes
• Membrane Proteins
• +4 more

Prof. Daniel Merkle is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on computational systems chemistry, algorithmic methods for chemical reaction networks, and graph-based approaches in computational biology. He is actively involved in interdisciplinary projects addressing microbiome-driven diseases and computational systems chemistry. Merkle has held leadership roles in EU-funded initiatives and international research networks, including the COST Actions on complex chemical systems and origins of life. He contributes to teaching committees and organizes international conferences like the Dagstuhl Seminar on Algorithmic Cheminformatics. His research interests span chemical graph rewriting, pathway realizability, reaction database curation, and algorithmic design for biochemical systems. Recent work includes advancements in synthetic chemistry frameworks (e.g., ChemReservoir), efficient extraction of reaction rules (SynTemp), and formal modeling of unbalanced chemical reactions using ITS Graphs. Merkle’s projects, such as MATOMIC and TACsy, address microbial community metabolism and train computational systems chemists. He has published over 110 research outputs and actively engages in peer review and editorial roles. Key collaborations include work with Flamm, Stadler, and international research teams on computational methods for chemical systems. His contributions to open-source tools and algorithmic frameworks have advanced both theoretical and applied aspects of systems chemistry.

• Chemical Informatics
• Computational Chemistry
• Graph Theory
• +3 more

Rolf Fagerberg is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU). His research centers on algorithms, data structures, and their applications in computational biology and cheminformatics. His research interests include: Algorithms and data structures, particularly dynamic and geometric data structures Graph theory with emphasis on subgraphs, Yao graphs, and hypergraphs Algorithmic cheminformatics and modeling of chemical reaction networks Computational biology, including metabolic pathway analysis Theoretical computer science and discrete mathematics Recent publications highlight a strong trend toward interdisciplinary research combining computer science with chemistry and biology, particularly in modeling chemical reaction networks using hypergraphs and mixed-integer linear programming. His work also includes algorithmic solutions for palindromic subsequence problems and efficient extraction of reaction rules from large databases, reflecting a blend of theoretical and applied algorithm design. Rolf Fagerberg has served as senior coordinator on multiple research projects, including 'Algorithmic Cheminformatics' and 'Fundamental Data Structures', funded by the Danish Ministry of Higher Education and Research. He has also contributed to peer review and editorial work for major conferences such as the ACM Symposium on Parallelism in Algorithms and Architectures and the International Symposium on Experimental Algorithms. He has supervised PhD students and is actively involved in academic service, including membership in assessment committees at Aarhus University and IT University of Copenhagen. His research has received media attention, including coverage of a 'mathematical breakthrough' and applications in understanding intestinal systems in obesity.

• Algorithms
• Data Structures
• Computational Biology
• +7 more

Jan Halborg Jensen is a Professor in the Department of Chemistry at the University of Copenhagen, with a distinguished research career in computational chemistry and molecular modeling. His work spans quantum chemistry, cheminformatics, and machine learning applications in chemical research, with significant contributions to drug design, catalyst discovery, and chemical reaction prediction. His primary research interests focus on Computational Chemistry , Molecular Modeling , and Quantum Chemistry , with particular expertise in applying machine learning techniques to chemical problems. Jensen's research integrates computational methods with practical chemical applications, including retrosynthesis planning, catalyst design, and drug discovery. His work bridges theoretical chemistry with practical applications in organic synthesis and pharmaceutical development. Analysis of Jensen's recent publications (2023-2025) reveals a strong focus on machine learning applications in chemistry, particularly for predicting chemical reactivity, designing catalysts, and advancing drug discovery. His work demonstrates increasing integration of artificial intelligence with traditional computational chemistry methods, with significant contributions to cheminformatics, transition metal chemistry, and biomolecular simulation standards. The research spans theoretical developments and practical applications across organic chemistry, biochemistry, and materials science. Jensen maintains an active research program with extensive publication output, including 88 research contributions comprising journal articles, book chapters, and books. His work shows strong collaborative networks across computational chemistry and related fields. He has presented on educational technology topics, including a lecture titled "Make a difference - teach and learn with technology" in April 2018, demonstrating engagement with pedagogical developments alongside research activities. His online presence includes a professional blog (molecularmodelingbasics.blogspot.com) and comprehensive CV documentation.

• Computational Chemistry
• Molecular Modeling
• Quantum Chemistry
• +4 more

Maria Harris Rasmussen is a Postdoctoral Researcher in the Department of Chemistry at the University of Copenhagen, where she conducts cutting-edge research at the intersection of computational chemistry, cheminformatics, and artificial intelligence. Her work focuses on developing and applying computational methodologies to solve complex chemical problems, with particular emphasis on reaction discovery, molecular representation, and catalyst design. Her research interests span multiple domains of computational chemistry, with primary focus on Cheminformatics where she develops algorithms for molecular representation including SMILES notation for transition metal complexes. In Quantum Chemistry , she investigates photoinduced electron transfer processes and reaction mechanisms using advanced simulation techniques. Her work in Machine Learning for Chemistry includes developing explainable AI methods for molecular property prediction and uncertainty quantification in chemical data sets. She also contributes significantly to Catalysis Research through computational approaches for de novo catalyst discovery and reaction screening. Analysis of her publication record reveals strong trends in developing computational tools that bridge theoretical chemistry with practical applications. Her recent work shows increasing integration of machine learning with traditional quantum chemical methods, particularly in the areas of reaction space exploration and catalyst discovery. The interdisciplinary nature of her research connects chemistry with computer science, physics, and data science, reflecting the evolving landscape of modern computational chemistry. Maria maintains active research collaborations with prominent scientists including Jensen J.H., Mikkelsen K.V., and several international researchers as evidenced by her publication record. Her computational methodologies have gained attention across academic and research communities, with multiple publications receiving significant readership on platforms like Mendeley and social media engagement.

• Computational Chemistry
• Cheminformatics
• Quantum Chemistry
• +5 more

Julius Johannes Seumer is a Postdoctoral Research Fellow in the Department of Chemistry at the Faculty of Science, University of Copenhagen. His research focuses on computational approaches to catalyst design and chemical synthesis optimization, with expertise in quantum mechanics simulations and evolutionary algorithms. His primary research interests include computational chemistry, catalysis, quantum chemical modeling, and machine learning applications in chemical synthesis. Key areas involve regioselectivity prediction, transition metal complex analysis, nitrogen fixation catalysts, and automated synthesis planning using genetic algorithms. Analysis of his recent publications reveals strong trends in computational catalyst discovery, particularly using genetic algorithms for exploring chemical space beyond traditional libraries. His work spans organic, inorganic, and physical chemistry domains with emphasis on Morita-Baylis-Hillman reactions, C-H activation, and dinitrogen fixation. Seumer collaborates extensively with Professor Jens H. Jensen and other researchers at the University of Copenhagen, with publications appearing in high-impact journals including Angewandte Chemie , Chemical Science , and Beilstein Journal of Organic Chemistry . His computational methodologies have garnered attention across academic networks, with citations in Scopus and engagement on platforms including X (Twitter), Bluesky, and Mendeley. His work demonstrates significant interdisciplinary impact across chemistry and computational sciences.

• Computational Chemistry
• Catalysis
• Quantum Chemistry
• +3 more