Prediction of biodegradability of aromatics in water using QSAR modeling

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water

Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R² = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C═O > O═C-O > OH > CH₃ > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pK_a and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

Structure of the flavonoid catechin in solution: NMR and quantum chemical investigations

DFT-PCM statistical index scan curves and ¹H-NMR profiles reveal conformational changes when a solid catechin sample is dissolved in acetone solvent.

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, Q²_loo = 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.

Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study

Extensive commercial use of aromatic hydrocarbons results with significant amounts of these chemicals and related by-products in waters, causing a severe ecological and health threat, thus requiring an increased attention. This study was aimed at developing models for prediction of the initial toxicity of the aromatic water-pollutants (expressed as EC₅₀ and TU₀) as well as the toxicity of their intermediates at half-life of the parent pollutant (TU_1/2). For that purpose, toxicity toward Vibrio fischery was determined for 36 single-benzene ring compounds (S-BRCs), diversified by the type, number and position of substituents. Quantitative structure-activity relationship (QSAR) methodology paired with genetic algorithm optimization tool and multiple linear regression was applied to obtain the models predicting the targeted toxicity, which are based on pure structural characteristics of the tested pollutants, avoiding thus additional experimentation. Upon derivation of the models and extensive analysis on training and test sets, 4-, 4- and 5-variable models (for EC₅₀ and TU₀, TU_1/2, respectively) were selected as the most predictive possessing 0.839<R²< 0.901 and 0.789<Q²< 0.859. The analysis of the selected descriptors indicated three major structural characteristics influencing the toxicity: electronegativity, geometry and electrotopological states of the molecule. Degradation kinetics determining as well the pathways of intermediates formation, reflected over ionization potential, was found to be an important parameter determining the toxicity in half-life.

Key structural features promoting radical driven degradation of emerging contaminants in water

Diverse contaminants of emerging concern (CECs) can be found in nowadays aquatic environment, possessing high potential to cause adverse ecological and human health effects. Due to their recalcitrance, conventional water treatment methods are shown to be inadequately effective. Thus, their upgrade by advanced oxidation processes, involving the generation of highly reactive species (HO and SO₄^-), is highly demanded. In order to assess the susceptibility of CECs by HO and SO₄^-, as well as to determine the corresponding reaction rate constants k_HO and k_SO4^-, the complex experimental studies has to be maintained. The alternative is the application of modeling approaches which correlate structural characteristics with activities/properties of interest, i.e. quantitative structure activity/property relationship (QSAR/QSPR). In this study k_HO and k_SO4^- of fifteen selected CECs were determined by competitive kinetics, and afterward used to elucidate key structural features promoting their degradation. In that purpose, QSPR models were constructed using multiple linear regression (MLR) combined with genetic algorithm (GA) approach. The models were submitted to the internal and external validation (using additional set of 17 CECs). Selected 3-variable models predicting k_HO and k_SO4^- were characterized with high accuracy and predictivity (R² = 0.876 and Q² = 0.847 and R² = 0.832 and Q² = 0.778, respectively). Although selected models at the first sight include descriptors derived through complicated calculation procedures, their weighting schemes indicate on their relevance and transparency toward established reaction theories and differences regarding radical type.

Prediction of biopersistence of hydrocarbons using a single parameter

Aerobic biodegradation is an important attenuation process for petroleum hydrocarbons (PHCs) in the natural environment. It has also been frequently used in engineered systems to remediate PHC-contaminated sites. A model such as a quantitative structure property relationship (QSPR) that can predict the biodegradation rate of PHCs would be helpful prior to implementing any extensive environmental measurements and bioremediation strategies. Existing QSPRs either have a large number of predictor variables that may cause overfitting or are based on a small dataset of PHCs. The goal of this study is to develop a simple, portable QSPR that has only a few predicator variables but can accurately predict the biodegradation half-lives of a large group of PHCs. To this end, more than 500 molecular variables were screened, and candidate variables were refined by a feature selection method and fitted to biodegradation data of a group of structurally heterogeneous PHCs (n = 173). The model was established by means of hierarchical clustering and classification and regression tree algorithms, which was optimized by an internal validation procedure and validated by an external dataset. The optimal QSPR model, containing only one predictor variable (the number of bonds that do not contain hydrogen), was able to accurately predict biodegradation half-lives for a wide variety of PHCs. The internal validation test indicated an overall prediction accuracy of 93%, and predictions applied to an independent external set of 64 PHCs yielded 95% accuracy. The new model is transparent and easily portable from one user to another.

Manual or automated measuring of antipsychotics' chemical oxygen demand

Antipsychotic (AP) drugs are becoming accumulated in terrestrial and aqueous resources due to their actual consumption. Thus, the search of methods for assessing the contamination load of these drugs is mandatory. The COD is a key parameter used for monitoring water quality upon the assessment of the effect of polluting agents on the oxygen level. Thus, the present work aims to assess the chemical oxygen demand (COD) levels of several typical and atypical antipsychotic drugs in order to obtain structure-activity relationships. It was implemented the titrimetric method with potassium dichromate as oxidant and a digestion step of 2h, followed by the measurement of remained unreduced dichromate by titration. After that, an automated sequential injection analysis (SIA) method was, also, used aiming to overcome some drawbacks of the titrimetric method. The results obtained showed a relationship between the chemical structures of antipsychotic drugs and their COD values, where the presence of aromatic rings and oxidable groups give higher COD values. It was obtained a good compliance between the results of the reference batch procedure and the SIA system, and the APs were clustered in two groups, with the values ratio between the methodologies, of 2 or 4, in the case of lower or higher COD values, respectively. The SIA methodology is capable of operating as a screening method, in any stage of a synthetic process, being also more environmentally friendly, and cost-effective. Besides, the studies presented open promising perspectives for the improvement of the effectiveness of pharmaceutical removal from the waste effluents, by assessing COD values.

Biodegradability of several antipsychotic drugs: manual and automatic assessment

For the first time, the biodegradability values were determined for several antipsychotic drugs.