Harnessing Topological Indices in QSPR: Predicting Molecular Properties for Parkinson's Disease Therapeutics

RAGHU S; Niranjan  K M; Venkanagouda  M Goudar; Shilpa Sasimath; Nikhath Banu

doi:10.5269/bspm.80928

Auteurs-es

RAGHU S GM University
Niranjan K M Department of Mathematics, UBDT College of Engineering, Davangere, A Constituent College of Visvesvaraya Technological University, Belagavi, Karnataka-577002, India.
Venkanagouda M Goudar Department of Mathematics, Sri Siddhartha Institute of Technology, A Constituent College of Sri Siddhartha Academy of Higher Education, Tumkur, Karnataka-572105, India
Shilpa Sasimath Department of Mathematics, UBDT College of Engineering, Davangere, A Constituent College of Visvesvaraya Technological University, Belagavi, Karnataka-577002, India.
Nikhath Banu Department of Mathematics, UBDT College of Engineering, Davangere, A Constituent College of Visvesvaraya Technological University, Belagavi, Karnataka-577002, India.

DOI :

https://doi.org/10.5269/bspm.80928

Résumé

Topological indices are numerical invariants derived from molecular graphs play a crucial role in Quantitative Structure-Property Relationship (QSPR) modeling, helping in the prediction of molecular properties relevant to drug discovery. In this study, we predicted eight physicochemical characteristics (PC-C) of thirteen Parkinson’s illness medicines through QSPR analysis and topological indices. We computed these indices using Python-based tools (RDKit, NetworkX) and performed regression analysis using Scikit-learn and SciPy. The results show that how some topological indices exhibit strong correlations with several PC-C, demonstrating their value in drug development pipelines.