A Random Forest Model for Peptide Classification Based on Virtual Docking Data.

ABSTRACT: The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed

SUBMITTER: Feng H 

PROVIDER: S-EPMC10380188 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature