This PhD project at the Max Planck Institute for Molecular Genetics focuses on the computational and evolutionary design of multi-target peptide inhibitors for respiratory viruses. The research group specializes in computational drug design, virtual screening, and in silico methods to explore ligand-protein interactions, with a strong track record in antivirals and enzyme inhibition. The project aims to develop evolutionary algorithms for peptide design and optimization, targeting multiple viral proteins responsible for common respiratory infections. Students will engage in structure-based analysis of mutational and structural data, and develop a scoring function for peptide activity, which will be iteratively validated through synthesis and experimental testing. The interdisciplinary environment offers collaboration with experts in computational drug design and experimental research, access to state-of-the-art computational resources, and opportunities for career development through conferences, workshops, and international collaborations. Applicants should have a Master’s degree in Pharmacy, Biochemistry, Chemistry, or a related field, with excellent grades and expertise in molecular modeling, chemistry, and machine learning. Experience with Python, TensorFlow, PyTorch, and virtual screening techniques is highly valued. The position is fully funded, and the application deadline is January 7, 2026.

This PhD project at the Max Planck Institute for Molecular Genetics focuses on the computational design of tailored modulators for the Kappa Opioid Receptor (KOR), a critical target in the treatment of chronic pain, mood disorders, and addiction. Building on recent research, the project aims to develop novel KOR modulators using structure-based drug design (SBDD) and potentially machine learning techniques, with a particular emphasis on optimizing functional selectivity (signaling bias). The research will draw inspiration from the binding mode of Salvinorin A, a known KOR ligand, and will involve the identification and in vitro testing of new modulators to assess their biological activity and receptor selectivity. Experimental feedback will be used to refine computational models, enabling further optimization of lead compounds. Molecular dynamics simulations will be employed to analyze the interaction profiles between KOR and its ligands, as well as to investigate the effects of different binding modes on downstream signaling pathways. The successful candidate will join an interdisciplinary research environment, collaborating with experts in computational drug design and experimental research, and will have access to state-of-the-art computational resources for molecular modeling, virtual screening, and machine learning. The position offers opportunities for career development, including participation in conferences, workshops, and international collaborations. Applicants should have a Master’s degree in Pharmacy, Biochemistry, Chemistry, or a related field with excellent grades, and demonstrate interest and expertise in molecular modeling, structure-based design, programming (Python, RDKit), and virtual screening techniques. The position is fully funded, and the application deadline is January 7, 2026.