Generation wise ranking of PDI core dendrimers for drug encapsulation informed by Gaussian process regression based QSPR analysis

Abstract

Abstract In this research, structural interaction analysis of Perylene Diimide (PDI) core dendrimers is conducted over four therapeutic and imaging classes. Core topology was mathematically encoded using topological descriptors, which include Leap Zagreb indices and polymer path Wiener polarity index. Drug generation encapsulation suitability was quantified through global feature normalization and standardized Euclidean distance, yielding compatibility scores used to rank dendrimer generations ( $$D_0$$ - $$D_4$$ ) for each drug without altering drug order or identity. To assess the stability and systematicity of generation suitability trends, intergenerational behavior was evaluated using monotonic rank association via Spearman’s rank correlation and global compatibility magnitude through relative changes in mean compatibility. To determine which topological indices most effectively explain specific physicochemical properties of drugs, that are, molecular weight, surface polarity, hydrogen bonding capacity, rotatable bond flexibility, $$\pi$$ -complexity, and structural intricacy, Gaussian Process Regression (GPR) models were developed within a Quantitative Structure-Property Relationship (QSPR) framework using a descriptor space comprising four therapeutic drug-related chemical indices. Model outputs were carefully analysed to evaluate descriptor property relevance using prediction error metrics and correlation statistics, allowing us to determine the most informative and relevant topological index for each chemical attribute. These results demonstrate that the employed topological descriptors provide a systematic graph-based representation for generation wise compatibility and exhibit property-dependent relationships with diverse physicochemical characteristics.

The authors' abstract, as published at the source. Network Modeling Analysis in Health Informatics and Bioinformatics, 2026 · DOI ↗