Machine Learning and Structural Analysis for Generation-Wise Selection of Perylene Diimide Dendrimers Based on Topological Compatibility With Drug Molecules

Machine learning is increasingly used to explore structure-property relationships in dendrimer-drug systems; however, its integration with topology-based structural compatibility analysis remains limited. This study presents a learning-driven framework to evaluate the topology-based structural compatibility of perylene diimide core dendrimers with antiviral, antioxidant, and imaging agents. Within a quantitative structure-property relationship framework, linear regression, Gaussian process regression (GPR), and artificial neural networks are employed to identify dominant graph-theoretic topological indices and to capture linear and nonlinear structure-property relationships. These insights are combined with a structure-based compatibility analysis that ranks dendrimer generations without altering drug identity or ordering. Generation-wise compatibility is computed using standardized Euclidean distance after global normalization, and ranking stability is assessed through monotonic and magnitude analysis. Results show class-dependent descriptor relevance, with GPR achieving test-set R² values above 0.94 for molecular weight, exact mass, monoisotopic mass, and heavy atom count. The generation-wise compatibility analysis further reveals distinct topology-based structural compatibility patterns across the investigated drug classes, highlighting the value of integrating machine learning with graph-theoretic analysis for rational nanocarrier design and the computational assessment of potential encapsulation suitability.