Prediction and Structure–Activity Relationship Analysis on Ready Biodegradability of Chemical Using Machine Learning Method

Abstract

Persistent contaminants from different industries have already caused significant risks to the environment and public health. In this study, a data set containing 1306 not readily biodegradable (NRB) and 622 readily biodegradable (RB) chemicals was collected and characterized by CORINA descriptors, MACCS fingerprints, and ECFP_4 fingerprints. We utilized decision tree (DT), support vector machine (SVM), random forest (RF), and deep neural network (DNN) to construct 34 classification models that could predict the biodegradability of compounds. The best model (model 5F) built using a Transformer-CNN algorithm had a balanced accuracy of 86.29% and a Matthews correlation coefficient of 0.71 on the test set. By analyzing the top 10 CORINA descriptors used for modeling, the properties containing solubility, π/σ atom charges, rotatable bonds number, lone pair/π/σ atom electronegativities, molecular weight, and number of nitrogen atom based hydrogen bonding acceptors were determined to be critical for biodegradability. The substructure investigations confirmed earlier studies that the presence of aromatic rings and nitrogen or halogen substitutions in a molecule will hinder the biodegradation of the compound, while the ester groups and carboxyl groups promote biodegradability. We also identified the representative fragments affecting biodegradability by analyzing the frequency differences of substructural fragments between the NRB and RB compounds. The results of the study can provide excellent guidance for the discovery and design of compounds with good chemical biodegradability.

Introduction to the Special Issue: AI Meets Toxicology

“…The methods described in this special issue cover a wide range of AI methods ranging from expert systems, ,, over similarity measures including read-across methods, − to classical machine learning such as random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) − ,, to deep learning (DL) methods ,,− , including equivariant neural networks, deep generative models, and even large language models . In addition to models relying purely on the chemical structure, there is a notable trend of bringing in additional modalities to improve or inform predictive models. , In the following, we provide an overview of the AI approaches used in the publications contained in the SI.…”

“…Most of the presented methods perform prediction tasks on molecules (for exceptions, see paragraphs below) and, thus, have to use representations of molecules. While for data handling, the simplified molecular-input line-entry system (SMILES) representation is often used, the predominant representations of molecules for modeling are still extended connectivity fingerprint (ECFP) or Morgan fingerprints. − ,− Several publications use graph neural networks that operate on the molecular graph. , Aside from the chemical structure, there is a growing tendency to incorporate biological characterizations and read-outs, for example, via cell morphology , or transcriptomics . The utilization of diverse representations, ranging from molecular structures to biological features, enhances the predictive models showcased in this section and could improve the comprehensive understanding of toxicological properties.…”