Improvement of biodegradability prediction QSAR system through optimal combination of algorithms

Abstract This paper introduces a novel quantitative structure-activity relationship (QSAR) system for predicting the biodegradability of chemicals. The system focuses on evaluating the ratio of biochemical oxygen demand (BOD) to theoretical oxygen demand (ThOD) (%BOD/ThOD) and the presence of degradation products to categorize chemicals as “readily biodegradable” (RB) or “not readily biodegradable” (NB). The conventional laboratory testing method for biodegradability assessment is time-consuming and expensive, thus highlighting the need for a quantitative structure-activity relationship (QSAR) system that can predict biodegradability without the need for extensive testing. The authors acquired a dataset of 3948 chemicals from the National Institute of Technology and Evaluation (NITE) database, which included information on molecular structure, %BOD/ThOD values, and the presence of degradation products. The QSAR system consists of two prediction models: a %BOD/ThOD prediction model and a discrimination prediction model. Multiple algorithms were employed in each model to enhance prediction accuracy. The %BOD/ThOD prediction model utilizes eight algorithms, including linear, nonlinear, and tree-structured models. The discrimination prediction model employs ten algorithms, and the outcomes are combined using majority voting. Explanatory variables for both models include molecular descriptors and spatial structure derived from the chemical’s molecular representation. The performance of the QSAR system was evaluated based on training and validation datasets. Results showed that the combination of three %BOD/ThOD prediction algorithms improved the average correction rate compared to using all algorithms. Similarly, selecting the discrimination prediction algorithm further enhanced the average correction rate.