معرفی
Professor Leif Eriksson is a leading computational chemist in the Department of Chemistry and Molecular Biology at the University of Gothenburg, holding his professorship since 2011 after positions at NUI Galway and Dalhousie University. His research group comprises 1 research engineer, 2 postdocs, 1 doctoral student, and multiple Master's researchers focusing on AI-driven drug discovery.
His educational background includes:
- Chemistry studies at Stockholm University and University of Sussex
- PhD in Quantum Chemistry, Uppsala University (1992)
- Postdoctoral Fellowship at Dalhousie University, Canada (1992-93)
Research centers on theoretical biophysical chemistry with emphasis on computational modeling of DNA damage, cancer mechanisms, and antibiotic development. The group pioneers AI-based tools (MolAI, iScore) for ultrafast drug discovery, studying stress response pathways in glioblastoma/TNBC and developing inhibitors through multi-scale simulations from QM/MM to molecular dynamics. Recent work targets protein-protein interactions, membrane diffusion, and drug-loaded liposomes using integrated computational-experimental approaches.
Publication trends (2022-2025) reveal strong focus on cancer therapeutics (35%), antibiotic discovery (25%), and AI-driven drug design (40%), with increasing integration of machine learning for ADMET prediction and target validation. Key journals include Journal of Chemical Information and Modeling (30%), Biochemistry (20%), and Scientific Reports (15%).
Scientific recognition includes:
- Göta Student Union's Pedagogical Prize (2017)
- 300+ scientific papers and 20+ review chapters
- Patents for skin cancer/glioblastoma drugs leading to spin-offs (ANYO Labs, C26 Bioscience, Cell Stress Discoveries)
Advising involves direct supervision of doctoral/postgraduate researchers with strong industry-academia pipelines. Grant funding is sustained through:
- National: Swedish Research Council, Swedish Cancer Society, Lawski Foundation
- International: Wenner-Gren Foundations, GBM Foundation, EU H2020 MSCA-RISE
Research infrastructure leverages national supercomputing resources (via NAC leadership) and integrates wet-lab validation through spin-off partnerships. Current projects focus on translating computational discoveries into patentable therapeutics for aggressive cancers and antimicrobial resistance, with multiple compounds in preclinical development phases.
