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 R ² 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.