Predicting Chemical Biodegradability for Sustainable Chemical Manufacturing: A Machine Learning Approach Using 3D Molecular Descriptors
Authors:   Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia, Elsayad Alaa , Ahmed Hassan ,   Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia, Elsayad Khaled ,   Pharmacy Department, Cairo University Hospitals, Cairo University, Cairo 11662, Egypt, Hassan Ammar ,   Electronic Systems Software Engineering Department, Faculty of Engineering, University of Science and Technology, 12211, Sudan, Mustafa Mustafa ,   Computer Engineering Department, College of Engineering, Future University, 12211, Sudan, Khan Akhtar ,   Department of Electrical Engineering, University of Engineering & Technology, Peshawar, Jalozai Campus, 25120, Pakistan, Ali Arif ,   Computer Science Department, Faculty of Mathematical & Computer Science, University of Gezira, 21111, Sudan, Mokhtar Sahar ,   Computer and Systems Department, Electronic Research Institute, Cairo 11662, Egypt
Published: January 2025

Achieving sustainable cities and promoting responsible consumption require innovative approaches to chemical design and manufacturing. Precise prediction of chemical biodegradability is crucial for evaluating environmental concerns and facilitating the transition towards green chemistry. This study investigates the effectiveness of ten distinct groups of three-dimensional (3D) molecular descriptors for classifying compounds with rapid biodegradability. The Merck molecular force field (MMFF94s) was used to compute descriptors and generate 3D conformations for a dataset of chemical compounds. The dataset underwent rigorous preprocessing, including feature selection, outlier management, and scaling. Support Vector Machines (SVMs) were tested alongside three tree-based ensemble learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest. Bayesian optimization was employed to optimize model hyperparameters and enhance cross-validated Area Under the Receiver Operating Characteristic Curve (AUC).
The GETAWAY descriptors, 3D autocorrelation descriptors, and 3D-MoRSE descriptors consistently demonstrated superior performance compared to other descriptors across all machine learning models. An SVM model trained on 3D autocorrelation descriptors achieved the highest prediction accuracy (0.88), sensitivity (0.83), specificity (0.91), F1-score (0.82), Cohen’s Kappa statistic (0.74), and an AUC of 0.93 on an independent test set. Advanced analytical techniques, including Permutation Feature Importance (PFI), SHapley Additive exPlanations(SHAP), and partial dependency plots (PDP) were utilized to identify the most influential 3D autocorrelation descriptors. The findings
of this study demonstrate that 3D molecular descriptors, particularly 3D autocorrelations, play a critical role in developing accurate and interpretable models for predicting chemical biodegradability. These models contribute significantly to the advancement of green chemical
design and the development of effective regulatory policies that support the objectives of SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production). By fostering sustainable chemical manufacturing practices, we can create healthier and more resilient urban environments while minimizing the environmental impact of human activities.