Integrating Molecular Dynamics Simulation & Machine Learning for Cyclic Peptide Structure Prediction
Time: 8:50 am
day: Conference Day One
Details:
- A significant obstacle preventing the rational or structure-based design of cyclic peptides is that little solution structural information is available for these molecules, since most cyclic peptides adopt multiple conformations in solution, existing as structural ensembles
- Developing the StrEAMM (Structural Ensembles Achieved by Molecular dynamics and Machine learning) platform for cyclic peptides by combining molecular dynamics simulation and machine learning to provide efficient, high-quality cyclic peptide structure predictions
- Rapidly predicting cyclic peptide structures to enable researchers to understand the structural basis for the diverse properties of cyclic peptides and accelerate the development of this unique class of molecules
