Peptides have emerged as promising candidates in therapeutics and
diagnostics due to their unique properties. They offer advantages over traditional small
molecule drugs, including high specificity, reduced off-target effects, biocompatibility,
and biodegradability. However, peptide-based therapeutics also present challenges of
low stability, delivery, synthesis, membrane permeability, and oral availability. Many
strategies such as cyclisation, incorporation of N- and C- terminal protecting groups,
and non-standard amino acid design of appropriate peptide delivery systems have been
adopted to mitigate these challenges. Computational techniques enable faster design
and development and reduce experimental costs involved in drug discovery and design
and therefore, have gained prominence for in-silico testing and development of
peptides. This chapter explores methods involved in the computational design of
therapeutic peptides, with significant attention to peptide-specific molecular docking
tools, lead optimization strategies, and Molecular Dynamics (MD) simulations. We
also discuss the applicability of peptides in biomedicine and review specialized peptide
databases, exemplified by the case study of the PepEngine. A compendium of Machine
Learning (ML) tools used in peptide drug design highlights the latest advances in the
field. Peptide-based therapeutics are highly promising due to their lower
bioaccumulation and toxicity along with high specificity. Many peptide-based drugs,
such as insulin, oxytocin, and enfuvirtide, have been widely accepted for therapeutic
applications. By mitigating the challenges faced in peptide design and aiding in the
development of novel therapeutic peptides, computational approaches have played an
instrumental role in the peptide drug development process.
Keywords: Computational drug design, Computational drug development, Computational peptide design, In-silico peptide analysis, Peptides, Peptide design, Peptide docking, Peptide databases, Peptide stability, Peptide delivery.
