Computational, Evolutionary Design of Multi-Target Peptide Inhibitors for Respiratory Viruses
Prof. Dr. Gerhard Wolber
We specialize in computational drug design, virtual screening, and in silico methods to explore ligand-protein interactions. Our lab has successfully applied these techniques in various therapeutic areas, including antivirals and enzyme inhibition. One key area of interest is designing peptide-based inhibitors that can target multiple viral proteins simultaneously, which are responsible for common respiratory infections. In this PhD project, we will focus on developing evolutionary algorithms for peptide design and optimization. This will involve a detailed, structure-based analysis of mutational and structural data, and the development of a scoring function for activity, which will be iteratively validated and refined through the synthesis and testing of the predicted peptides.
Requirements:
- Master’s degree in Pharmacy, Biochemistry, Chemistry, or a related field with excellent grades
- Interest and expertise in molecular modeling, chemistry, and machine learning
- Experience and/or interest in machine learning frameworks (TensorFlow, PyTorch), Python, and virtual screening techniques
What we offer:
- Interdisciplinary Research Environment: You will collaborate closely with experts in computational drug design, and experimental research, gaining experience in a collaborative and dynamic research setting.
- Cutting-Edge Research Infrastructure: Access to state-of-the-art computational resources for molecular modeling, virtual screening, and machine learning.
- Opportunities for Career Development: Participation in conferences, workshops, and exposure to international collaborations to further develop your expertise and career in the field.
More info on the research group
© Gerhard Wolber