Quantitative structure-biodegradability relationships (QSBRs) using modified autocorrelation method (MAM)

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Toxicology

Background:

• Biodegradability is a key factor in environmental risk assessment.
• Predictive models for biodegradability can streamline environmental safety evaluations.
• Quantitative Structure-Biodegradability Relationships (QSBRs) offer a computational approach to predict chemical biodegradability.

Purpose of the Study:

• To establish quantitative structure-biodegradability relationships (QSBRs) for various organic compounds.
• To develop predictive models for biodegradability using molecular descriptors.
• To assess the utility of autocorrelation components and physicochemical properties in QSBR modeling.

Main Methods:

• Molecular descriptors including van der Waals volume, electronegativity, hydrogen bonding ability, and lipophilicity (log P) were calculated.
• Autocorrelation components were employed as molecular descriptors.
• QSBR models were developed for specific compound classes (alcohols, ketones, aromatics) and for a combined set of compounds.

Main Results:

• Models were established for alcohols, ketones, and aromatics.
• A predictive model for the 5-day Biochemical Oxygen Demand (BOD) of alcohols and ketones was developed (n=29, r=0.958).
• A broader model for all tested compounds was also established (n=43, r=0.906), demonstrating good predictive power.

Conclusions:

• QSBRs using autocorrelation components and physicochemical properties are effective for predicting organic compound biodegradability.
• The developed models provide valuable tools for environmental risk assessment and chemical design.
• The study highlights the importance of molecular descriptors in understanding and predicting environmental fate processes.