Hydrophobicity versus electrophilicity: A new protocol toward quantitative structure-toxicity relationship

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Toxicology

Background:

• Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) models are crucial for predicting chemical compound properties.
• Understanding the biological properties of substituted benzene derivatives is essential for drug discovery and safety assessment.

Purpose of the Study:

• To develop and validate QSAR/QSPR/QSTR models for substituted benzene derivatives.
• To introduce a novel descriptor, the square of electrophilicity index (ω²), for improved structure-activity relationship analysis.

Main Methods:

• Utilized Quantitative Structure-Activity Relationship (QSAR), Quantitative Structure-Property Relationship (QSPR), and Quantitative Structure-Toxicity Relationship (QSTR) modeling.
• Proposed and evaluated a novel descriptor: the square of electrophilicity index (ω²).
• Employed neural networks (NN) to validate models developed using multiple linear regression (MLR).

Main Results:

• The square of electrophilicity index (ω²) demonstrated a compact and effective correlation between chemical structure and biological properties.
• This novel descriptor showed marginally superior performance compared to the electrophilicity index (ω) or ω³.
• The performance of ω² was comparable to hydrophobicity/lipophilicity descriptors.
• Neural network analysis confirmed the robustness of the QSAR models.

Conclusions:

• The square of electrophilicity index (ω²) is a valuable descriptor for QSAR/QSPR/QSTR modeling of substituted benzene derivatives.
• The findings support the use of advanced computational methods, including neural networks, for robust biological property prediction.
• This approach can aid in the efficient design and safety evaluation of chemical compounds.