COMPUTATIONAL MODELING FOR PREDICTING ANTIBIOTIC RESISTANCE RISK IN SOIL ECOSYSTEM

Yoggita S, Vaishnavi V Rao, Anusha A Vernakar, Sindhu Mallappa Gavannavar, Veeksha K V

Antibiotic contamination in soil ecosystems has emerged as a major environmental concern due to its role in accelerating antimicrobial resistance (AMR). This review aims to evaluate computational modeling approaches used to predict antibiotic behavior and resistance risk in soil environments. A structured literature review was conducted using peer-reviewed articles focusing on sorption, persistence, and toxicity modeling. Computational approaches such as QSAR, pedotransfer functions, and machine learning models were analyzed. The findings indicate that antibiotic fate in soil is strongly influenced by environmental factors such as pH and organic carbon content. Hybrid modeling approaches integrating molecular descriptors and environmental parameters showed improved predictive accuracy. Additionally, even low concentrations of antibiotics were found to contribute to resistance development through genetic mechanisms. Computational models are effective tools for predicting AMR risk in soil ecosystems. However, improved datasets and integrated modeling frameworks are required to enhance prediction reliability and environmental relevance.

Keywords –Antibiotic residues; Antimicrobial resistance; Ecotoxicity; Persistence; Soil ecosystem; Sorption; Computational modelling.