QSAR Modeling of Sphingomyelin Synthase 2 Inhibitors for Their Potential as Anti-Atherosclerotic Agents

Abstract

Sphingomyelin synthase 2 (SMS2) has emerged as a promising target for atherosclerosis threatment. However, the availability of selective SMS2 inhibitors and their associated pharmacological properties remains limited. This research paper explores various QSAR modeling techniques applied to a range of compounds acting as SMS2 inhibitors. Multiple distinct QSAR modeling methodologies were employed, including conformation-independent, GA-MLR and 3D based QSAR modeling, and their mutual correlations were investigated, Various statistical methods were applied to assess the quality, robustness, and predictive capacity of these developed models, yielding favorable results. Furthermore, molecular fragments derived from SMILES notation descriptors, which account for the observed changes in the evaluated activity, were defined. The methodology presented in this research holds potential for identifying novel agents for atherosclerosis treatment by targeting sphingomyelin synthase 2.