Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Preclinical drug discovery involves extensive, costly experiments.
• Quantitative Structure-Activity Relationship (QSAR) modeling and machine learning have improved efficiency.
• QSAR utilizes experimental data to predict biological activity of novel compounds.

Purpose of the Study:

• To review Multiple Linear Regression (MLR)-based QSAR studies.
• To assess compound toxicity towards Pimephales promelas and Tetrahymena pyriformis using CDFT-electrophilicity index (ω).
• To evaluate Human African Trypanosomiasis (HAT) curing activity of pyridyl benzamide derivatives.

Main Methods:

• Utilized global conceptual density functional theory (CDFT)-based electrophilicity index (ω) as a descriptor.
• Compared electrophilicity index (ω) with hydrophobicity parameter (logP).
• Employed Multiple Linear Regression (MLR) for QSAR modeling.

Main Results:

• Electrophilicity index (ω) was used to predict toxicity and HAT activity.
• QSAR models incorporated electrophilicity (ω, ω²) and hydrophobicity (logP, (logP)²) parameters.
• The study highlights the utility of CDFT descriptors in drug discovery.

Conclusions:

• QSAR modeling, particularly with CDFT-derived descriptors like electrophilicity, offers a more efficient approach to predict compound activity and toxicity.
• Electrophilicity index (ω) provides a computationally accessible alternative to logP for QSAR studies.
• This review consolidates findings on QSAR applications in predicting environmental toxicity and therapeutic efficacy.