Deep learning-guided design and theorety-based understanding of peptidic scaffolds to combat antimicrobial resistance

Eötvös Loránd Tudományegyetem

Hevesy György Kémia Doktori Iskola

Beke-Somfai Tamás

kémiai tudományok

The Biomolecular Self-assembly “Momentum” research group ( http://bionano.ttk.hu/) is looking for a PhD student in chemistry, focusing on molecular simulations and development of deep learning protocols for non-natural peptide design. The practical motivation is the rapid spread of the antimicrobial resistance due to overuse of small molecule-based antibiotics. The project would implement standard methods of molecular simulation, e.g. molecular dynamics, quantum mechanics, to study peptidic assemblies and their properties when complexed to target microbial membrane components. In addition, in close cooperation with the experimentalist synthesizing peptides in our group, and mice experiments performed on these compounds, the applicant would develop a deep learning protocol that could adapt existing database of natural peptides to de novo design of non- natural molecules.
For reference see some of our recent works: El Battioui et al. 2024. Nat. Commun. 15 3424
Wacha et al. 2023, JCIM, 63, 12, 3799 and recent literature: Liu et al. 2025, Nat. Mat. 24, 1295

1 fő

ELTE, Doctoral School of Chemistry

2026-05-31

English