Structure-retention relationship study of arylpiperazines by linear multivariate modeling.

Area of Science:

• * Analytical Chemistry
• * Medicinal Chemistry
• * Computational Chemistry

Background:

• * Understanding the relationship between molecular structure and chromatographic retention is crucial for drug discovery and development.
• * Arylpiperazines are a significant class of compounds with diverse pharmacological activities.
• * Thin-layer chromatography (TLC) offers a cost-effective method for separating and analyzing chemical compounds.

Purpose of the Study:

• * To establish quantitative structure-retention relationships (QSRR) for 33 newly synthesized arylpiperazines.
• * To identify key molecular descriptors influencing chromatographic behavior.
• * To compare the predictive performance of multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS) regression models.

Main Methods:

• * Synthesis of 33 novel arylpiperazine compounds.
• * Chromatographic separation and retention measurement using thin-layer chromatography (TLC).
• * Application of chemometric methods: Principal Component Analysis (PCA) followed by MLR, PCR, and PLS regression.

Main Results:

• * PLS regression demonstrated the best statistical performance, yielding the lowest standard errors (RMSEc=0.159, RMSEcv=0.231).
• * Key molecular descriptors influencing retention included surface tension, hydrophilic-lipophilic balance, polar surface area, and hydrophilic surface area.
• * The PLS model incorporated logP, while PCR included hydrogen bond descriptors and LUMO energy; MLR utilized topological descriptors.

Conclusions:

• * PLS regression is a highly effective method for modeling QSRR of arylpiperazines.
• * Surface tension and hydrophilic-lipophilic balance are critical factors governing the chromatographic retention of these compounds.
• * The developed QSRR models provide valuable insights for the rational design and synthesis of novel arylpiperazines with desired properties.