Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

ABSTRACT:

Background

Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far; however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce.

Results

First, we adopted sparse modeling and small molecule information to construct a PPB prediction model for cycli

SUBMITTER: Tajimi T 

PROVIDER: S-EPMC6311893 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature