Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.

ABSTRACT: In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available. Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instea

SUBMITTER: Li J 

PROVIDER: S-EPMC8796384 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature