Hybrid CNN-CatBoost Approach for Predicting Biodegradability of Synthetic Chemicals

The field of environmental chemistry is essential for understanding the impacts of synthetic chemicals on ecosystems. The increasing prevalence of these substances poses significant challenges to ecosystem health, particularly regarding their biodegradability. Non-biodegradable materials threaten wildlife and human health, emphasizing the need for effective predictive methods. Traditional laboratory assessments of biodegradability are often laborintensive and costly, making it difficult to efficiently evaluate large numbers of compounds. To address these challenges, Quantitative Structure-Activity Relationship (QSAR) models have been developed to establish correlations between chemical structures and biodegradability [1]. Nevertheless, it is challenging to characterize complicated interactions using conventional QSAR techniques that are present in numerous chemical databases [4]. The proposed model integrates Convolutional Neural Network (CNN) with CatBoost, an advanced gradient boosting technique. This hybrid method effectively handles categorical variables and reduces overfitting while employing CNN to extract relevant features from molecular descriptors and improve predictive accuracy [2]. By implementing the CNN-CatBoost hybrid methodology, biodegradability predictions are significantly improved, facilitating better environmental management and helping to mitigate the challenges posed by chemical pollution.