Self-assembling peptides bear tremendous potential in the fields of material sciences, nanoscience, and medicine. In contrary to the popular building blocks used in supramolecular chemistry, which exploit rigid molecular structures with defined geometry, peptides are highly flexible. This feature renders the prediction of their most stable conformations and self-assembly ability, as well as an understanding of the mechanism behind aggregation, more challenging for experimental techniques. In this context, in silico techniques have progressed at a fast pace to provide highly valuable tools to study, predict, and visualize peptides’ behavior and their dynamics to assist with their design. In this chapter, we will provide an overview of popular computational techniques used to investigate the self-assembly of peptides and peptide-containing molecules. Together with the applications, we will briefly discuss the pros and cons of these methodologies and conclude with a perspective on the future directions that this exciting field can lead to.
