Quantitative Structure-biodegradability Relationships Research Articles

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models

This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.

An improved opposition-based crow search algorithm for biodegradable material classification

ABSTRACT The development of a reliable quantitative structure–activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure–biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.

Study on quantitative structure-biodegradability relationships of amine collectors by GFA-ANN method

In this study, the biodegradability of 17 amine collectors, categorized by fatty amine, quaternary ammonium compounds and oxygen-containing amine collectors, are tested with the Closed Bottle Test for 90 days, and the results indicate most amine collectors are not readily biodegradable. The oxygen-containing amine collectors have the best biodegradability due to the introduced oxygen-containing functional groups, subsequently fatty amine collectors with branched chains, while the tested quaternary ammonium compounds all have poor biodegradation ability. Besides, we search for and calculate 35 molecular descriptors to develop the quantitative structure biodegradability relationship (QSBR) of amine collectors. With the Genetic Function Approximation (GFA) algorithm, two sets of important molecular descriptors related to biodegradability (q) of amine collectors are selected from 35 molecular descriptors. Based on internal and external validations, the robust and reliable non-linear QSBR model with the squared correlation coefficient above 0.99 is determined via Artificial Neural Network (ANN) method, where the descriptors are respectively CL, N, ELUMO, δv2, indicating the biodegradable ability of amine collectors is correlated with the alkyl chain lengths (CL), the number of nitrogen atom-containing compounds (N), energy of the lowest unoccupied molecular orbital (ELUMO) and valence second-order connectivity index (δv2).

Comparison of Small Molecule Biotransformation Half-Lives between Activated Sludge and Soil: Opportunities for Read-Across?

Compartment-specific degradation half-lives are essential pieces of information in the regulatory risk assessment of synthetic chemicals. However, their measurement according to regulatory testing guidelines is laborious and costly. Despite the obvious ecological and economic benefits of knowing environmental degradability as early as possible, its consideration in the early phases of rational chemical design is therefore challenging. Here, we explore the possibility to use half-lives determined in highly time- and work-efficient biotransformation experiments with activated sludge and mixtures of chemicals to predict soil half-lives from regulatory simulation studies. We experimentally determined half-lives for 52 structurally diverse agrochemical active ingredients in batch reactors with three concentrations of the same activated sludge. We then developed bi- and multivariate models for predicting half-lives in soil by regressing the experimentally determined half-lives in activated sludge against average soil half-lives of the same chemicals extracted from regulatory data. The models differed in how we accounted for sorption-related bioavailability differences in soil and activated sludge. The best-performing models exhibited good coefficients of determination (R2 of around 0.8) and low average errors (<factor of 3 in half-life predictions) and were robust in cross-validation. From a practical perspective, these results suggest that it may indeed be possible to read across from half-lives determined in highly efficient biotransformation experiments in activated sludge to soil half-lives, which are obtained from much more work- and resource-intense regulatory studies, and that these predictions are clearly superior to predictions based on the output of BIOWIN, a publicly available quantitative structure-biodegradation relationship (QSBR) model. From a theoretical perspective, these results suggest that soil and activated sludge microbial communities, although certainly different in terms of taxonomic composition, may be functionally similar with respect to the enzymatic transformation of environmentally relevant concentrations of a diverse range of chemical compounds.

A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

The objective of this work was to develop a QSBR model for the prioritization of organic pollutants based on biodegradation rates from a database containing globally harmonized biodegradation tests using relevant molecular descriptors. To do this, we first categorized the chemicals into three groups (Group 1: simple aromatic chemicals with a single ring, Group 2: aromatic chemicals with multiple rings and Group3: Group 1 plus Group 2) based on molecular descriptors, estimated the first order biodegradation rate of the chemicals using rating values derived from the BIOWIN3 model, and finally developed, validated and defined the applicability domain of models for each group using a multiple linear regression approach. All the developed QSBR models complied with OECD principles for QSAR validation. The biodegradation rate in the models for the two groups (Group 2 and 3 chemicals) are associated with abstract molecular descriptors that provide little relevant practical information towards understanding the relationship between chemical structure and biodegradation rates. However, molecular descriptors associated with the QSBR model for Group 1 chemicals (R2 = 0.89, Q2loo = 0.87) provided information on properties that can readily be scrutinised and interpreted in relation to biodegradation processes. In combination, these results lead to the conclusion that QSBRs can be an alternative tool to estimate the persistence of chemicals, some of which can provide further insights into those factors affecting biodegradation.

Prediction of Biodegradability for Polycyclic Aromatic Hydrocarbons Using Various In Silico Modeling Methods.

Polycyclic aromatic hydrocarbons (PAHs) have attracted great concern as global environmental pollutants. In this work, the quantitative structure-biodegradability relationship (QSBR) study has been done to predict the biodegradability of PAHs and develop the correlation between the biodegradability and the molecular structures. The structural chemistry and quantum chemistry descriptors were used to represent molecular structures. Three in silico modeling methods, i.e., multiple linear regression (MLR), radial basis function neural network, and back-propagation artificial neural network (BPANN), are utilized to construct the linear and nonlinear prediction models and provide some insights into the structural characteristics affecting the biodegradability of PAHs. The stability of these QSBR models was tested by leave-one-out cross-validation, and the cross-validated correlation coefficients (q2) were 0.6109, 0.6887, and 0.6586, respectively. The correlation coefficients (R2) of the three models for the training set were 0.7811, 0.8883, and 0.9667, respectively. The comparison of the three models showed that the BPANN model produced a statistically more significant model than the other two models. On the basis of molecular structure, the dominant molecular structure descriptor affecting biodegradability of PAHs were analyzed and discussed.

Ultrasonic-assisted biodegradation of endocrine disrupting compounds by Pseudomonas putida the importance of rhamnolipid for intermediate product degradation

The present study aimed to completely remove estrogens, including oestrone(E1), oestradiol(E2), oes-triol(E3), 17α-ethinylestradiol(EE2) and bisphenol-A(BPA), from soil using Pseudomonas putida(P. putida). A central composite design was developed to determine the optimal conditions of three variables(ultrasonication time, quantity of P. putida, and concentration of added rhamnolipid) for the removal of the estrogens, and the biodegradation rates of the estrogens were investigated under the optimum conditions. Moreover, a quantitative structure-biodegradation relationship(QSBR) was used to analyze the effect of the estrogenic physicochemical properties on the enhancement of the biological degradation. The optimal conditions were an ultrasonication time of 3 min, a P. putida quantity of 8 mL, and a rhamnolipid concentration of 100 mg/L. These conditions resulted in removal of 100%, 94.86%, 94.90%, 96.56% and 94.56% of E1, E2, EE2, BPA and E3, respectively after 7 d. The degradations were more rapid and com-plete than those reported in previous studies, indicating the suitability of the adaptation of P. putida to estrogen de-gradation under conditions of ultrasonic-assistance and adding rhamnolipid; improvement was particularly apparent from the complete degradation of E3. Based on a Pearson correlation analysis, the estrogen molecule polar surface area(PSA) and surface tension were significantly related to the biodegradation effect. An analysis of the QSBR model with the estrogen biodegradation rates as a dependent variable and the PSA and surface tension as independent va-riables indicated that larger PSA caused decreased estrogen biodegradation, while the biodegradation progress was dominated by the surface tension of the estrogens. The interaction of PSA and surface tension had an antagonistic ef-fect on the biodegradation of estrogens. Therefore, rhamnolipid/ultrasonication can significantly improve the biode-gradation rates of oestrogens in soil, while simultaneously adjusting other environmental conditions would influence and control the biodegradation processes of estrogens.

Development of QSBR models for anoxic biodegradability of polycyclic aromatic hydrocarbons by using SMLR and BP-ANN

Abstract In the present study, quantitative structure-biodegradability relationship (QSBR) models were developed for the anoxic Andrews model parameters of polycyclic aromatic hydrocarbons (PAHs). The molecular geometries of 20 PAHs were studied using density functional theory. Stepwise multiple linear regression (SMLR) and backpropagation artificial neural network (BP-ANN) methods were applied to establish the QSBR model between the anoxic biodegradability (q max ) and 5 molecular descriptors. Results of the regression analysis indicated that both the accuracy and predictive ability of the BP-ANN model were better than those of SMLR model. After analyzing the sensitivity of variables, the key molecular structure descriptor influencing anoxic biodegradability of PAHs were screened to be E HOMO and Freq. The present study demonstrates the value of QSBR not only as a predictive tool but also as a framework for understanding the mechanisms governing biodegradation at the molecular level.

In Silico Based Prediction and Correlation of Dehalogenase Enzyme Activity of Some Haloalkane Compounds by Quantitative Structure Biodegradation Relationship (QSBR) Analysis

In Silico Based Prediction and Correlation of Dehalogenase Enzyme Activity of Some Haloalkane Compounds by Quantitative Structure Biodegradation Relationship (QSBR) Analysis

Anoxic degradation of nitrogenous heterocyclic compounds by activated sludge and their active sites

The potential for degradation of five nitrogenous heterocyclic compounds (NHCs), i.e., imidazole, pyridine, indole, quinoline, and carbazole, was investigated under anoxic conditions with acclimated activated sludge. Results showed that NHCs with initial concentration of 50mg/L could be completely degraded within 60hr. The degradation of five NHCs was dependent upon the chemical structures with the following sequence: imidazole>pyridine>indole>quinoline>carbazole in terms of their degradation rates. Quantitative structure–biodegradability relationship studies of the five NHCs showed that the anoxic degradation rates were correlated well with highest occupied molecular orbital. Additionally, the active sites of NHCs identified by calculation were confirmed by analysis of intermediates using gas chromatography and mass spectrometry.

Quantitative structure–biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper, stepwise multiple linear regression analysis method was applied to establish quantitative structure biodegradability relationship (QSBR) between the chemical structure and a novel biodegradation activity index (qmax) of 20 polycyclic aromatic hydrocarbons (PAHs). The frequency B3LYP/6-311+G(2df,p) calculations showed no imaginary values, implying that all the structures are minima on the potential energy surface. After eliminating the parameters which had low related coefficient with qmax, the major descriptors influencing the biodegradation activity were screened to be Freq, D, MR, EHOMO and ToIE. The evaluation of the developed QSBR mode, using a leave-one-out cross-validation procedure, showed that the relationships are significant and the model had good robustness and predictive ability. The results would be helpful for understanding the mechanisms governing biodegradation at the molecular level.

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure–biodegradation relationship (SBR) and quantitative structure–biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

Quantitative structure–biodegradability relationship of sulfide mineral flotation collectors

12 physicochemical and quantum chemical parameters of 7 kinds of sulfide mineral flotation collectors are calculated. On the basis of the experimental data, a QSBR model including parameters, such as E LUMO , μ, TE, (E LUMO − E HOMO ) and (E LUMO − E HOMO ) 2 was established for K b prediction by multiple linear regression. Results showed that the calculated data fitted well with the experimental values, indicating the reliable prediction and good robustness of the developed model, which can be used in K b prediction of analogs. According to analysis of the model, the electrical parameters were the dominant factors affecting the aerobic biodegradability of sulfide mineral flotation collectors. However, the steric parameters and hydrophobic parameters had a small impact on biodegradability of the collectors.

2D and 3D quantitative structure-biodegradability relationship study of phenol derivatives

Based on the experimental data of production of carbon dioxide (PCD) of phenol derivatives, a 2D-QSBR model was established between PCD and quantum chemistry parameters on phenol derivatives which were calculated at B3LYP/6-311G** level. The 2D-QSBR model contains two variables: molecular average polarizability ( α ) and standard entropy ( S Θ). α has greater effect on PCD than SΘ. The correlation coefficients ( R 2) and cross-validation correlation coefficients ( q 2) of the 2D-QSBR model at B3LYP/6-311G** level are 0.933 and 0.894, respectively, showing the priority of this model over models based on midix and 6-31G* level. Correspondingly, a 3D-QSBR model was also established using comparative molecular similarity index analysis (CoMSIA). The R 2 and q 2 of CoMSIA model are 0.964 and 0.716, respectively. The 3D contour maps suggested that the H-bonding donor and H-bonding acceptor factors have greater impact on PCD, and hydrophobic factors also have significant effect on PCD. The result indicates that the 2D-QSBR and 3D-QSBR models have strong stability and good predictability.

Biodegradable Naphthenic Acid Ionic Liquids: Synthesis, Characterization, and Quantitative Structure–Biodegradation Relationship

It has been confirmed that commonly used ionic liquids are not easily biodegradable. When ultimately disposed of or accidentally released, they would accumulate in the environment, which strongly restricts large-scale industrial applications of ionic liquids. Herein, ten biodegradable ionic liquids were prepared by a single, one-pot neutralization of choline and surrogate naphthenic acids. The structures of these naphthenic acid ionic liquids (NAILs) were characterized and confirmed by (1)H and (13)C NMR spectroscopy, IR spectroscopy, and elemental analysis, and their physical properties, such as densities, viscosities, conductivities, melting points (T(m)), glass transition points (T(g)), and the onset temperatures of decomposition (T(d)), were determined. More importantly, studies showed that these NAILs would be rapidly and completely biodegraded in aquatic environments under aerobic conditions, which would make them attractive candidates to be utilized in industrial processes. To explore the underlying mechanism involved in the NAIL biodegradation reaction and seek prediction of their biodegradability under environmental conditions, four molecular descriptors were chosen: the logarithm of the n-octanol/water partition coefficient (log P), van der Waals volume (V(vdW)), energies of the highest occupied molecular orbital (E(HOMO)), and energies of the lowest unoccupied molecular orbital (E(LUMO)). Through multiple linear regression, a general and qualified model including the biodegradation percentage for NAILs after the 28-day OECD 301D test (%B(28)) and molecular descriptors was developed. Regression analysis showed that the model was statistically significant at the 99 % confidence interval, thus indicating that the %B(28) of NAILs could be explained well by the quantum chemical descriptor E(HOMO), which might give some important clues in the discovery of biodegradable ionic liquids of other kinds.

Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships (QSBRs) were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight (Mw), energies of the highest occupied molecular orbital ( E HOMO), the lowest unoccupied molecular orbital ( E LUMO), and the excited state (EES), calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that E HOMO and Mw were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of E HOMO and M W. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Aromatic compounds biodegradation under anaerobic conditions and their QSBR models

Anaerobic biodegradability of 46 kinds of aromatic compounds was tested and assessed in integrate. These aromatic compounds were classified into readily, partially and poorly biodegradable compounds after their integrated assessment indices (IAI) were calculated. Some rules of anaerobic biodegradation of them were drawn. Stepwise regression and backpropagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure biodegradability relationship (QSBR) based on the assessment results. In QSBR models, five molecular structure descriptors, energy of the highest occupied molecular orbital (EHOMO), total energy (TolE), molar refractivity (MR), the logarithm of the partition coefficient for n-octanol/water (Log P), and standard Gibbs free energy ( G), were included. After analyzing the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptors affecting anaerobic biodegradability of aromatic compounds were TolE and MR, which were in direct proportion to the anaerobic biodegradability of aromatic compounds.

Biodegradability of nitrogenous compounds under anaerobic conditions and its estimation

The anaerobic biodegradability of 23 nitrogenous compounds, including nitrogenous heterocyclic compounds and aliphatic amines, was tested and assessed in integration. These nitrogenous compounds were classified into readily, partially, and poorly biodegradable compounds after calculation of their integrated assessment indices (IAIs), Rules for anaerobic biodegradation of these compounds were also drawn. Stepwise regression and backpropagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure–biodegradability relationships (QSBRs) based on the assessment results. In QSBR models, three molecular structure descriptors—second-order molecular connectivity index ( 2 χ v 3 χ p v ), and energy of the highest occupied molecular orbital (EHOMO)—were included. After analysis of the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptor affecting anaerobic biodegradability of nitrogenous compounds is EHOMO, which is directly proportional to the anaerobic biodegradability of nitrogenous compounds.

農薬の環境残留性-データの収集と構造特徴解析

A data set of 420 pesticide persistences in the environment was collected as field half-life (HL) using several on-line databases. Due to the fuzziness of observed values, the compounds were grouped into three major categories: class 1 when a pesticide has HL ≤ 30 days; class 2, if 30 < HL ≤ 100 days; and class 3, if HL > 100 days. The Quantitative Structure-Biodegradation Relationship (QSBR) analysis was worked out on the training set of 315 pesticides. Thirty one topological substructural descriptors were used and the decision tree approach was employed for the modeling. Estimation results were as follows: for the train set, 5 compounds of two-unity (class 1/class 3) misclassification, 38 compounds of unity (class 1/class 2 and class 2/class 3) misclassification, and 272 compounds (86.3%) were correctly classified; for the test set, there were 3, 20, and 82 compounds (78.1%) respectively. The computer expert system EKeeper was developed on the basis of the QSBR model.

Anaerobic biodegradability of aliphatic compounds and their quantitative structure biodegradability relationship

In this study, anaerobic biodegradability of 47 kinds of aliphatic compounds was tested and assessed in integrate. These aliphatic compounds were classified into readily, partially and poorly biodegradable compounds after their integrated assessment indices (IAI) were calculated. Some laws of anaerobic biodegradation of aliphatic compounds were drawn. Stepwise regression and back propagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure biodegradability relationship (QSBR) based on the assessment results. In QSBR models, six molecular structure descriptors were included. After analyzing the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptors affecting anaerobic biodegradability of aliphatic compounds were total energy (TolE) and molecular diameter ( D), which were in direct proportion to the anaerobic biodegradability of aliphatic compounds.