Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides.

ABSTRACT: Tumor-homing peptides (THPs) have emerged as promising agents in cancer treatments. These short sequences can specifically target tumor cells and vasculature. Here, a nontrained machine learning (ML) method based on network science and multiquery similarity searching to predict THPs is presented. We leverage the network-based representation of THPs' chemical space to extract valuable information by employing a novel similarity-based, yet sparse, network known as the half-space proximal network (HSPN). The HSPN of the THPs' giant component is composed of 12 communities that represent distinct modes of action and/or targets, as well as sequence templates (scaffolds). In the HSPN analysis, various centrality measures were employed to identify the most significant and nonredundant THPs. These

SUBMITTER: Romero M 

PROVIDER: S-EPMC12631478 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature