topological descriptor analysis with chemical graph theory insights and predicted adme data analysis for pentafluorophenylurea-based pyridine derivative

The compound n, n’-pyridine-2,6-diylbis-[3-(pentafluorophenyl) urea] (pdpf) was synthesized [ccdc deposition number:2385135] and characterized using single crystal xrd and ft-ir techniques. structural analysis confirmed intra-molecular and inter-molecular hydrogen bonding, with crystal data revealing a triclinic system and space group pī. this study delves into the structural and chemical properties of the title compound, n, n’-pyridine-2,6-diylbis-[3-(pentafluorophenyl) urea], also known as 1-(perfluorophenyl)-3-(6-(3-(2,3,4,5,6-pentafluorophenyl) ureido) pyridin-2-yl) urea, through the lens of chemical graph theory and a diverse set of topological indices. by calculating and analysing 16 distinct topological descriptors, this research offers a comprehensive perspective on the compound’s molecular topology, electronic distribution, and potential chemical behaviour. to validate the predictive power of these descriptors, data from advanced bioinformatics tools like swissadme, swisssimilarity ranking, and swisstargetprediction were integrated, providing a robust framework for understanding the compound’s chemical and biological properties. specifically, the swissadme tool is used to predict adme properties like solubility, lipophilicity (logp), and drug-likeness, while swissdock provides docking scores to evaluate the compound’s binding affinities. in addition, swisstargetprediction is employed to assess potential biological targets and bioactivity profiles. our findings show good qualitative correlations between the topological descriptors and the predicted adme properties, and biological targets, demonstrating the power of chemical graph theory in predicting molecular behaviour. the results also suggest that some indices such as the wiener index, randic index, and zagreb indices are particularly effective at predicting lipophilicity and bioactivity, while the balaban index and hyper-wiener index are more closely linked with the compound’s docking interactions. this study provides a novel approach by integrating graph-theoretical descriptors with modern computational tools, offering a theoretical framework that can be further developed in future studies for drug design and molecular optimization. we wish to place on record that this is the first study of its kind for the title compound.