Machine Learning for De Novo Design of Macrocyclic Peptides as Strain-Specific
Abstract
In this proposal, we will integrate the recent advances from our labs to develop a high throughput machine-learning (ML) guided framework for the rapid design of macrocyclic peptides as affinity reagents against pathogenic strains of bacteria. We expect these new ML models will improve our design accuracy and provide a general framework for the rapid creation of proteome-targeting macrocycles. Overall, we aim to use our iterative design-synthesis-test-learn approach to develop a new machine learning framework for the custom design of new peptide affinity reagents as leads for diagnostics and therapeutic leads.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 14, 2022
- Source ID
- HDTRA12110007
Entities
People
- David Baker
Organizations
- Defense Threat Reduction Agency
- University of Washington