Quantitative structure-biotransformation relationships of organic micropollutants in aerobic and anaerobic wastewater treatments.

Biotransformation is one of the dominant processes to remove organic micropollutants (OMPs) in wastewater treatment. However, studies on the role of molecular structure in determining the biotransformation rates of OMPs are limited. We evaluated the biotransformation of 14 OMPs belonging to different chemical classes under aerobic and anaerobic conditions, and then explored the quantitative structure-biotransformation relationships (QSBRs) of the OMPs based on biotransformation rates using valid molecular structure descriptors (electrical and physicochemical parameters). Pseudo-first-order kinetic modeling was used to fit the biotransformation rate, and only 2 of the 14 OMPs showed that the biotransformation rate constant (kbio) values were higher under anaerobic conditions than aerobic conditions, indicating that aerobic conditions were more favorable for biotransformation of most OMPs. QSBRs infer that the electrophilicity index (ω) is a reliable predictor for OMPs biotransformation under aerobic conditions. ω corresponds to the interaction between OMPs and microbial enzyme active sites, this process is the rate-limiting step of biotransformation. However, under anaerobic conditions the QSBR based on ω was not significant, indicating that specific functional groups may be more critical than electrophilicity. In conclusion, QSBRs can serve as alternative tools for the prediction of the biotransformation of OMPs and provide further insights into the factors that influence biotransformation.

Can AI Help Improve Water Quality? Towards the Prediction of Degradation of Micropollutants in Wastewater.

Micropollutants have become a serious environmental problem by threatening ecosystems and the quality of drinking water. This account investigates if advanced AI can be used to find solutions for this problem. We review background, the challenges involved, and the current state-of-the-art of quantitative structure-biodegradation relationships (QSBR). We report on recent progress combining experiment, quantum chemistry (QC) and chemoinformatics, and provide a perspective on potential future uses of AI technology to help improve water quality.

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

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).

Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment.

Attenuation of organic compounds in sewage treatment plants (STPs) is affected by a complex interplay between chemical (e.g. ionization, hydrolysis), physical (e.g. sorption, volatilization), and biological (e.g. biodegradation, microbial acclimation) processes. These effects should be accounted for individually, in order to develop predictive cheminformatics tools for STPs. Using measured data from 70 STPs in the Netherlands for 69 chemicals (pharmaceuticals, herbicides, etc.), we highlighted the influences of 1) chemical ionization, 2) sorption to sludge, and 3) acclimation of the microbial consortia on the primary removal of chemicals. We used semi-empirical corrections for each of these influences to deduce biodegradation rate constants upon which quantitative structure-biodegradation relationships (QSBRs) were developed. As shown by a global QSBR, biodegradation in STPs generally relates to structural complexity, size, energetics, and charge distribution. Statistics of the global QSBR were reasonable, being R2training=0.69 (training set of 51 compounds) and R2validation=0.50 (validation set of 18 compounds). Class-specific QSBRs utilized electronic properties potentially relating to rate-limiting enzymatic steps. For class-specific QSBRs, values of R2 of in between 0.7 and 0.8 were obtained. With caution, environmental risk assessment methodologies may apply these models to estimate biodegradation rates for 'data-poor' compounds. The approach also highlights 'meta data' on STP operational parameters needed to develop QSBRs of better predictability in the future.

The experimental determination of reliable biodegradation rates for mono-aromatics towards evaluating QSBR models.

Quantitative Structure Biodegradation Relationships (QSBRs) are a tool to predict the biodegradability of chemicals. The objective of this work was to generate reliable biodegradation data for mono-aromatic chemicals in order to evaluate and verify previously developed QSBRs models. A robust biodegradation test method was developed to estimate specific substrate utilization rates, which were used as a proxy for biodegradation rates of chemicals in pure culture. Five representative mono-aromatic chemicals were selected that spanned a wide range of biodegradability. Aerobic biodegradation experiments were performed for each chemical in batch reactors seeded with known degraders. Chemical removal, degrader growth and CO2 production were monitored over time. Experimental data were interpreted using a full carbon mass balance model, and Monod kinetic parameters (Y, Ks, qmax and µmax) for each chemical were determined. In addition, stoichiometric equations for aerobic mineralization of the test chemicals were developed. The theoretically estimated biomass and CO2 yields were similar to those experimentally observed; 35% (s.d ±â€¯8%) of the recovered substrate carbon was converted to biomass, and 65% (s.d ±â€¯8%) was mineralised to CO2. Significant correlations were observed between the experimentally determined specific substrate utilization rates, as represented by qmax and qmax/Ks, at high and low substrate concentrations, respectively, and the first order biodegradation rate constants predicted by a previous QSBR study. Similarly, the correlation between qmax and selected molecular descriptors characterizing the chemicals structure in a previous QSBR study was also significant. These results suggest that QSBR models can be reliable and robust in prioritising chemical half-lives for regulatory screening purposes.

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.

Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline.

Hansch's model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch's analysis. We also developed a new type of PT-QSBR Hansch's model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

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.