Machine Learning Methods for Mineralization-Based Biodegradation Prediction in Polyhydroxyalkanoate-Based Biopolymers: Insights from Lab-Scale Experiments

The use of bio-based and biodegradable plastic products (BBpPs) ensures the mitigation of environmental effects of fossil-based plastics, especially in humanitarian crises where waste management is challenging. Polyhydroxyalkanoates (PHAs) are promising biodegradable biopolymers that are biocompatible and do not cause microplastic pollution. However, experimental assessment of PHA biodegradation is challenged by its time- and resource-intensiveness. In this study, a comprehensive computational Quantitative Structure–Activity Relationship (QSAR)-based approach was developed to predict biodegradability of short chain length (scl)-PHA-based formulations consisting of various additives and building blocks. A novel curated dataset for the (scl)-PHA poly(-3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), with literature-reported environmental and biodegradation parameters from lab-scale experiments in soil, marine, freshwater and compost systems, was constructed and used to develop and validate the introduced approach. Random forest (RF) and Extreme Gradient Boosting (XGBoost) machine learning (ML) models were optimized and validated with cross-validation and test set predictions. The optimal models reported high accuracy values of the coefficient of determination R2, indicating excellent relationships between structure and biodegradation metrics. Further analysis of descriptor variable importance confirmed that biopolymer biodegradability was favorably affected by biodegradation time, while mechanisms, environmental conditions, and additives contributed secondary yet physically consistent effects. The proposed QSAR framework demonstrated a robust and interpretable web-based tool for predicting the environmental fate of PHBV in natural environments and supported the sustainable safe-by-design (SSbD) approach of next-generation biodegradable polymers.