Beyond Traditional Descriptors: Innovative Topological Indices for Enhanced Molecular Predictions

Abstract

Aim/Background: In order to capture significant structural features of chemical compounds, this study presents three new topological indices: the Fragment-Weighted Connectivity Index (FWCI), the Distance-Weighted Subgraph Index (DWSI), and the Symmetry-Based Informational Index (SBI). In order to provide improved molecular descriptors for Quantitative Structure-Property Relationship (QSPR) modelling, these indices seek to represent molecular symmetry, spatial arrangement, and functional group connectivity. Materials and Methods: Alkanes, alcohols, and aromatic chemicals were among the datasets to which the proposed indices were applied. They were employed to simulate physicochemical characteristics such as log P, molecular weight, molecular volume, and boiling point. Through the use of these indices, QSPR models were created, and their prediction abilities were evaluated using k-fold cross-validation. Results: There was a substantial association between boiling points and FWCI, which measures fragment connectivity. Shape-related parameters like surface area and log P were successfully predicted by DWSI. Properties driven by symmetry, such as chirality and optical activity, were captured by SBI. When combined, these novel indices performed better than other conventional descriptors such as the Wiener and Randic indices, mainly for molecules with complicated structures. Conclusion: The findings show that FWCI, DWSI, and SBI improve prediction power for chemical characteristics and provide a deeper understanding of molecular behaviour. These new indices signify a major breakthrough in QSPR modelling and may find use in drug discovery and cheminformatics.