معرفی
Patrick Bryant is a Principal Investigator and DDLS Fellow at Stockholm University, affiliated with SciLifeLab. He leads a research group developing AI-driven tools for protein structure prediction, molecular interactions, and de novo design of functional biomolecules. His work includes frameworks like RareFold (for noncanonical amino acids) and EvoBind (cyclic peptide design), applied to therapeutic challenges such as HIV-1 inhibition and metabolic receptor targeting.
Research Interests
Bryant's research integrates deep learning with structural biology to enable:
- Prediction of protein complexes and alternative conformations
- Design of cyclic peptides for drug development
- Exploration of noncanonical amino acid chemistry
- Identification of pathogenic protein interfaces
Publication Trends
His recent articles (2023–2025) demonstrate a consistent focus on AI methodologies for protein engineering, with applications spanning antiviral design, cancer susceptibility, and protein-ligand interactions. Key themes include reinforcement learning for molecular assemblies, inverse folding techniques, and high-throughput prediction of biological networks.
Awards
- DDLS Fellowship
Advising and Team
Bryant mentors one PhD student (Diandra Daumiller) and collaborates with postdoctoral researcher David Li. His group focuses on scalable computational frameworks for molecular discovery.
