جست‌وجوی استاد و پژوهشگر

Olga Kalininaمشاهده پروفایل

استاد

• Bioinformatics
• Drug Discovery
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Olga Kalinina leads the Drug Bioinformatics research group at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). She holds an M.Sc. in Mathematics from Moscow State University (2003) and a Ph.D. in Bioinformatics from the Russian Academy of Sciences (2007). Her academic journey includes postdoctoral positions at the European Molecular Biology Laboratory (EMBL) and University of Heidelberg, followed by establishing her independent research group at the Max Planck Institute for Informatics (2012-2018). Her research program integrates cutting-edge bioinformatics and computational approaches to address challenges in drug discovery and resistance mechanisms. Primary research domains include: Development of machine learning tools for predicting functional consequences of genetic variants and drug resistance phenotypes Structural bioinformatics approaches for protein-drug interaction analysis Graph theory-based investigation of drug-binding pockets Protein language models enhanced with 3D structural information Explainable AI for drug-target interaction prediction Her group maintains active research projects on: Predicting molecular mechanisms of genetic variants in cardiac/renal diseases (BMBF-funded) Investigating off-target drug binding mechanisms (BMBF-funded) Antimicrobial resistance discovery using explainable AI (HelmholtzAI-funded) Protein structure-informed drug affinity prediction Prof. Kalinina has received the EMBO Long Term Fellowship and currently supervises seven doctoral students. Her laboratory develops computational resources including novel methods for solvent accessibility estimation (SphereCon), variant annotation in structural context (StructMAn), and protein interaction analysis (DIGGER).

• Machine Learning
• Drug Discovery
• Privacy-Preserving AI
• +۴ مورد دیگر

Dr. Matthias Becker serves as a Group Leader within the Career Development Fellow Programme at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany. His research integrates advanced machine learning techniques with biomedical applications, focusing on drug discovery and privacy-preserving health data analysis. His primary research interests include: Development of generative AI models for drug molecule design (DrugDiff) Privacy-preserving synthetic data generation for medical research (PriSyn project) Swarm learning applications for high-dimensional biomedical data Energy-efficient computing for molecular modeling His work bridges computational methods with neurodegenerative disease research, emphasizing practical implementations that balance innovation with ethical data handling. Becker's recent publication analyzes autoencoder architectures for molecular data, demonstrating significant reductions in data requirements (97%) and energy consumption (36%) while maintaining performance. His research shows particular strength in optimizing latent space utility for chemical applications. Becker actively collaborates with major institutions including CISPA Helmholtz Center for Information Security, QuantPi startup, and Hewlett Packard Enterprise. His technical expertise spans HPC/GPU cluster utilization, containerization for reproducibility, and emerging technologies like FPGAs for energy-efficient computing. He contributes to DZNE's computational infrastructure while advancing research in synthetic data generation for genetic and clinical applications.

• Computational Chemistry
• Molecular Modeling
• Quantum Chemistry
• +۴ مورد دیگر

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
• Cheminformatics
• Quantum Chemistry
• +۵ مورد دیگر

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
• Catalysis
• Quantum Chemistry
• +۳ مورد دیگر

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.