A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis.

ABSTRACT: Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS. Using a linear regression approach, we create models for Quantitative Structure-Property Relations (QSPR), detecting correlations between properties such as enthalpy of vaporization, flash point, molar weight, polarizability, molar volume, and complexity with certain degree related topological indices. We used a dataset related to drugs for MS with known properties for training the model and also

SUBMITTER: Farooq FB 

PROVIDER: S-EPMC11697749 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature