AI-Based Models for Prediction of Biodegradation
Ganesh B. Patil, Sopan Nangare, Shital M. Patil, Shankarsing Sardarsing Rajput, Milind M. Patil · Apple Academic Press eBooks · 2025
Artificial intelligence (AI) has the potential to enhance biodegradation prediction dramatically. AI plays a crucial role in biodegradation prediction by enabling the construction of models that predict the biodegradability of certain chemicals based on their structural properties. This chapter provides an overview of the current level of AI in biodegradation prediction as well as prospective future directions. It begins by reviewing the many AI 248 methodologies widely used for biodegradation prediction, such as machine learning algorithms, feature selection methods, and QSAR modeling. In constructing robust and effective AI models for biodegradation prediction, the chapter emphasizes the relevance of data quality, amount, and variety. The performance assessment metrics that are thoroughly evaluated for measuring the precision and reliability of AI models include accuracy, precision, recall, and F1-score. The limitations and constraints of AI in biodegradation prediction are also discussed, including data availability, model robustness, and ethical implications. The implications of AI in biodegradation prediction for environmental science and sustainability are also examined, including the possible influence on chemical risk assessment, environmental policymaking, and sustainable chemical design. The chapter finishes with recommendations for future AI research and applications in biodegradation prediction, such as the need for multidisciplinary cooperation, more data sharing, and improved model interpretability and transparency. This chapter is an excellent resource for scholars, practitioners, and policymakers interested in using AI approaches for environmental sustainability and pollution control. Its overarching purpose is to offer a comprehensive assessment of the existing situation, challenges, and prospective influence of AI in forecasting biodegradation.