Identifying potential binding protein targets for small molecules, such as substrates, transmitters, or drugs, is a major challenge in molecular biology and drug discovery alike. In biology, identifying the target protein is key for elucidating biochemical pathways and infection processes. In pharmacology, target identification for potential drug candidates derived from phenotypic screenings is indispensable for understanding the mechanism of action, exploring drug repurposing opportunities, and detecting potential side effects early on.

The substantial breakthroughs in computational structural biology enable the use of protein structure models alongside experimental structures (X-ray, cryo-EM) as the basis for a computational approach to target discovery. In this project, the doctoral candidate will apply and improve methods for structure-based target identification, tailoring them to specific target classes. To this end, classical molecular docking techniques and machine learning-driven approaches will be combined for predicting molecular complex structures. Based on heterogeneous data resources, new machine-learning models for target ranking will be developed. The resulting methods will be applied to a new class of anti-infective targets, with the opportunity for experimental validation.

Requirements:

• Master's degree in bioinformatics, cheminformatics, chemistry, biochemistry, or pharmacology with a strong computational orientation
  or Computer Science with a strong life science orientation
• good programming skills in Python, ideally combined with at least basic knowledge in C++
• experience with the use of modern IDEs, ideally under Linux
• experience with the predominant machine learning packages, e.g., PyTorch, sklearn, etc.
• knowledge in structural biology and related computational methods (use of molecular modeling, homology modeling, AI-based structure prediction methods)
• knowledge in physical chemistry, especially free energy of biomolecular systems and molecular interactions

Position:

• University of Hamburg
• 100% EGR. 13 (TV-L) position for three years

The University of Hamburg is committed to equity. Diversity enriches our university life, whether in our studies, research, teaching, education, or workplace. We therefore welcome all applications, regardless of gender, gender identity, sexual orientation, ethnic or social background, age, religion or belief, disability, or chronic illness.

The University of Hamburg strives to increase the number of women in academia, and encourages qualified female academics to apply.

Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.