Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.

Classification Model of Pesticide Toxicity in Americamysis bahia Based on Quantum Chemical Descriptors

A set of quantum chemical descriptors (molecular polarization, heat capacity, entropy, Mulliken net charge of the most positive hydrogen atom, APT charge of the most negative atom and APT charge of the most positive atom with hydrogen summed into heavy atoms) was successfully used to establish the classification models for the toxicity pLC50 of pesticides in Americamysis bahia. The optimal random forest model (Class Model A) yielded predictive accuracy of 100% (training set of 217 pesticides), 95.8% (test set of 72 pesticides) and 99.0% (total set of 289 pesticides), which were very satisfactory, compared with previous classification models reported for the toxicity of compounds in aquatic organisms. Therefore, it is reasonable to apply the quantum chemical descriptors associated with molecular structural information on molecular bulk, chemical reactivity and weak interactions, to develop classification models for the toxicity pLC50 of pesticides in A. bahia.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms

Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.

Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting

The creation of predictive models with a high degree of generalizability in chemical analysis and process optimization is of paramount importance. Nonetheless, formulating a prediction model based on collected data from chemical measurements that maximize quantitative generalizability remains a challenging task for chemometrics experts. To tackle this challenge, a range of forecasting models with varying characteristics, structures, and capabilities has been developed, utilizing either accuracy-based or reliability-based modeling strategies. While the majority of models follow the accuracy-based approach, a recently proposed reliability-based approach, known as the Etemadi approach, has shown impressive performance across various scientific fields. The Etemadi models were constructed through a reliability-based parameter estimation process in such a manner that maximizes the models' reliability. However, the foundation of modeling procedures for chemometrics purposes is built upon the assumption that high generalizability in inaccessible/test data is achieved through the accuracy-based training procedure in which errors in available historical/training data are minimized. After conducting a thorough review of the current literature, we have found that none of the forecasting models for chemometrics purposes incorporate reliability into their modeling procedures. Given the dynamic and highly sensitive nature of chemistry experiments and processes, implementing a reliable model that controls performance criteria variation is a promising strategy for achieving stable and robust forecasts. To address this research gap, this paper introduces several key innovations, which can be highlighted as follows: (1) Proposing a general design structure based on a new optimal reliability-based parameter estimation process. (2) Introducing a novel risk-based modeling strategy that minimizes the performance variation of models implemented under different conditions in chemical laboratory experiments, to generate a more generalizable model for diverse applications in chemometrics. (3) Specifying the degree of influence that each reliability and accuracy factor has in enhancing the generalizability and uncertainty modeling of chemometric models. Empirical evidence confirms the effectiveness and superior performance of reliability-based models compared to accuracy-based models in 78.95% of the cases across various fields, including Pharmacology, Biochemistry, Agrochemical, Geochemical, Biological, Pollutants, Physicochemical Properties, and Gases Experiment. Furthermore, the study's findings demonstrate that the reliability-based modeling approach outperforms the accuracy-based strategy in terms of MAE, MSE, ARV, and RMSE by an average of 4.697%, 5.646%, 5.646%, and 4.342%, respectively. It is also statistically proven that reliability has a more significant impact on improving the generalizability of chemometric models than accuracy. This emphasizes the importance of including reliability as a crucial factor in chemometrics modeling, a consideration that has been overlooked in traditional modeling processes. Consequently, reliability-based modeling approaches can be regarded as a viable alternative to conventional accuracy-based modeling methods for chemical modeling purposes.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβ_ML) and potentiometric sensitivity (PS_ML) of 200 ligands in complexes with the heavy metal ions Cu²⁺, Cd²⁺, and Pb²⁺. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R²) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R² of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Two QSAR models for predicting the toxicity of chemicals towards Tetrahymena pyriformis based on topological-norm descriptors and spatial-norm descriptors

Quantitative structure-activity relationship (QSAR) is important for safe, rapid and effective risk assessment of chemicals. In this study, two QSAR models were established with 1230 chemicals to predict toxicity towards Tetrahymena pyriformis using multiple linear regression (MLR) method. The topological(T)-QSAR model was developed by using topological-norm descriptors generated from the topological structure, and the spatial(S)-QSAR model were built with spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The r²_training and r²_test are 0.8304 and 0.8338 for the T-QSAR model, and 0.8485 and 0.8585 for the S-QSAR model, which means that T-QSAR model and S-QSAR model can be used to predict toxicity quickly and accurately. In addition, we also conducted validation on the developed models. Satisfying validation results and statistical parameters demonstrated that QSAR models based on the topological-norm descriptors and spatial-norm descriptors proposed in this paper could be further utilized to estimate the toxicity of chemicals towards Tetrahymena pyriformis.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R² (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis has been verified.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Support vector machine-based model for toxicity of organic compounds against fish

Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC₅₀ of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R² above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC₅₀ of organic compounds against various fish species is successful.

Quantitative structure-toxicity relationships of organic chemicals against Pseudokirchneriella subcapitata

Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC₁₀ of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R² of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R² of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC₁₀ shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC₅₀ of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.

Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models

A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R² of 0.86 and root mean square (rms) error of 0.41, and the test set has R² of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Evaluation of phenol-induced ecotoxicity in two model ciliate species: Population growth dynamics and antioxidant enzyme activity

The application of identical exposure dosages in different species generally leads to a limited understanding of dose-response patterns because of species-specific factors. To evaluate phenol-induced ecotoxicity, antioxidant enzyme activity and population growth dynamics were compared in two model ciliates, the marine species Euplotes vannus and the freshwater species Paramecium multimicronucleatum. Dosage ranges of phenol exposure were based on tolerance limits of test ciliates as determined by their carrying capacity (K) and growth rate (r). When the exposure duration of phenol increased from 48 h to 96 h, the median effective dose (ED₅₀) for P. multimicronucleatum decreased faster than that for E. vannus_, and the ratio of the former to the latter declined from 2.75 to 0.30. When E. vannus was exposed to increasing concentrations of phenol (0-140 mg l^-1), r rose initially and then dropped significantly at concentrations higher than 40 mg l^-1, whereas K decreased linearly over the entire range. For P. multimicronucleatum, both r and K declined gradually over the range 0-200 mg l^-1 phenol. Dose-response patterns of activities of three individual antioxidant enzymes, and the integrative index of the three enzymes, presented a biphasic (inverse U-shaped) curve at each of four durations of exposure, i.e. 12 h, 24 h, 36 h and 48 h. Cluster analyses and multidimensional scaling analyses of antioxidant enzyme activities revealed differences in the temporal succession of physiological states between the two model ciliates. In brief, combining ED₅₀ with growth dynamic parameters is helpful for designing exposure dosages of toxicants in ecotoxicity tests.

Predicting acute toxicity of traditional Chinese medicine wastewater using UV absorption and volatile fatty acids as surrogates

In this study, the applicability of UV absorbance at 254 nm (UV₂₅₄) and volatile fatty acids (VFAs) to serve as reliable surrogates to predict acute toxicity of traditional Chinese medicine (TCM) wastewater was investigated. The medicine residues and VFAs were identified as main components of the TCM wastewater, and their individual and joint toxicity assays were operated with luminescent bacteria. The median effective concentration (EC₅₀) values of medicine residues and VFAs were in the range of 26.46-165.55 mg/L and 11.45-20.58 g/L, respectively. The joint toxicity action modes of medicine residues, VFAs and medicine residues-VFAs were identified as additive, additive and synergistic respectively. UV₂₅₄ and VFAs showed better correlations with acute toxicity according to the correlation analysis, compared with other conventional parameters. The regression model was a good fit for toxic unit (TU₅₀) as a function of UV₂₅₄ and VFAs according to the stepwise regression method (adjusted R² = 0.836). Validation of the model to the pilot-scale samples provided satisfactory prediction results in the influent and hydrolysis acidification effluent samples tests, but for EGSB effluent and final effluent samples, the model needed further optimization. Surrogates prediction using UV₂₅₄ and VFAs provided a valuable and cost-saving tool for rapid or on-line monitoring of acute toxicity of TCM wastewater.

Construction of noninterpenetrating and interpenetrating Co(ii) networks with halogenated carboxylate modulated by auxiliary N-donor co-ligands: structural diversity, electrochemical and photocatalytic properties

Six Co(ii) complexes with 2,5-dichloroterephthalate and bis(benzimidazole) were synthesized and characterized, which manifest photocatalytic activities.

High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods

The environmental protection agency thinks that quantitative structure–activity relationship (QSAR) analysis can better replace toxicity tests.