Development Of Quantitative Structure-activity Relationship Models Research Articles

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

IntroductionThe current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata. MethodologyTo address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms. ResultsUniversal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay.Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models. ConclusionsThis study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles

Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure–activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R2test = 0.911, adjusted R2test = 0.733, RMSEtest = 0.091, and MAEtest = 0.067) and for the combination of internal and external datasets (R2test = 0.908, adjusted R2test = 0.871, RMSEtest = 0.255, and MAEtest = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.

MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins.

Mycotoxins are secondary metabolites produced by certain filamentous fungi. They are common contaminants found in a wide variety of food matrices, thus representing a threat to public health, as they can be carcinogenic, mutagenic, or teratogenic, among other toxic effects. Several hundreds of mycotoxins have been reported, but only a few of them are regulated, due to the lack of data regarding their toxicity and mechanisms of action. Thus, a more comprehensive evaluation of the toxicity of mycotoxins found in foodstuffs is required. In silico toxicology approaches, such as Quantitative Structure-Activity Relationship (QSAR) models, can be used to rapidly assess chemical hazards by predicting different toxicological endpoints. In this work, for the first time, a comprehensive database containing 4360 mycotoxins classified in 170 categories was constructed. Then, specific robust QSAR models for the prediction of mutagenicity, genotoxicity, and carcinogenicity were generated, showing good accuracy, precision, sensitivity, and specificity. It must be highlighted that the developed QSAR models are compliant with the OECD regulatory criteria, and they can be used for regulatory purposes. Finally, all data were integrated into a web server that allows the exploration of the mycotoxin database and toxicity prediction. In conclusion, the developed tool is a valuable resource for scientists, industry, and regulatory agencies to screen the mutagenicity, genotoxicity, and carcinogenicity of non-regulated mycotoxins.

Application of QSAR Approach to Assess the Effects of Organic Pollutants on Bacterial Virulence Factors.

The release of a wide variety of persistent chemical contaminants into wastewater has become a growing concern due to their potential health and environmental risks. While the toxic effects of these pollutants on aquatic organisms have been extensively studied, their impact on microbial pathogens and their virulence mechanisms remains largely unexplored. This research paper focuses on the identification and prioritization of chemical pollutants that increase bacterial pathogenicity, which is a public health concern. In order to predict how chemical compounds, such as pesticides and pharmaceuticals, would affect the virulence mechanisms of three bacterial strains (Escherichia coli K12, Pseudomonas aeruginosa H103, and Salmonella enterica serovar. Typhimurium), this study has developed quantitative structure-activity relationship (QSAR) models. The use of analysis of variance (ANOVA) functions assists in developing QSAR models based on the chemical structure of the compounds, to predict their effect on the growth and swarming behavior of the bacterial strains. The results showed an uncertainty in the created model, and that increases in virulence factors, including growth and motility of bacteria, after exposure to the studied compounds are possible to be predicted. These results could be more accurate if the interactions between groups of functions are included. For that, to make an accurate and universal model, it is essential to incorporate a larger number of compounds of similar and different structures.

2D-QSAR study and design of novel pyrazole derivatives as an anticancer lead compound against A-549, MCF-7, HeLa, HepG-2, PaCa-2, DLD-1

In this study, a local quantitative structure–activity relationship (QSAR) models were developed for set of compounds tested for their inhibitory activity against six different cancer cell lines viz. A-549, MCF-7, HeLa, HepG-2, PaCa-2 and DLD-1. Two different statistical approaches Principal Component Analysis (PCA) and Partial Least Square (PLS) analyses were employed to developed QSAR models. Further, activity predictions were carried out for in-house synthesized 63 pyrazole derivatives. Prediction of pIC50 value of all 63 synthesized pyrazole derivatives were estimated based on the most significant QSAR model developed for each cancer cell line. Several statistical parameters such as correlation coefficient R2, RMSE, Cross validated R2, Cross validated RMSE, internal validation Q2 and the external validation R2 revealed that developed models showed a significant value for explaining an acceptable QSAR model. The results derived highlighted some important compounds for being the most promise lead candidate against the six-cancer cell line with a significant pIC50 value. Considering the contribution of most important descriptors, we have designed new molecules which found to have greater inhibitory potentiality than the reference compounds. Overall, the results suggest that the developed QSAR models might be useful as a theoretical reference for experimental studies and designing more potent anti-cancer therapeutic pyrazoles based compounds.

Quantitative structure–activity relationship models for predicting apparent rate constants of organic compounds with ferrate (VI)

Ferrate (VI) (Fe (VI)) is a promising, environmentally friendly multifunctional oxidant widely applied in organic compound degradation. Oxidative kinetics of the apparent second-order rate constants (kapp) of Fe (VI) with organic compounds are critical for modeling oxidation processes. Herein, a quantitative structure–activity relationship (QSAR) model was developed using particle swarm optimization and an extreme learning machine to better understand the laws of the kapp values of organic compounds, including 33 aliphatic and aromatic hydrocarbon derivatives, during degradation by Fe (VI). Seven components—electronic hardness (H), electronic softness (S), ratio of oxygen to carbon atoms (On/Cn), energy of the highest occupied molecular orbital (EHOMO), vertical ionization potential (VIP), maximum nucleophilic reaction index (f(+)x), and minimum relative electrophilicity index (REn) constitute the critical molecular parameters. The developed QSAR model was verified on the basis of the coefficient of determination (R2) and the root mean square error (RMSE): for the training set, R2 = 0.924 and RMSE = 1.186, whereas for the test set, R2 = 0.996, and RMSE = 0.352. The applicability, reliability, and predictability of the model were verified by estimating the applicability domain (AD) of the model. Furthermore, QSAR models constructed using different methods were compared, and the main impact descriptors and conclusions obtained from previous studies were theoretically analyzed. Results indicate that constructing the QSAR model facilitates kapp prediction for Fe (VI) in the degradation of various organic compounds, improves the understanding of the degradation mechanism, and reduces the pressure on human and material resources caused by experiments.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R2) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors-An In Silico Screening Study.

In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding.

Predicting Endocrine Disruption Using Conformal Prediction - APrioritization Strategy to Identify Hazardous Chemicals with Confidence.

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

HDAC1 PREDICTOR: a simple and transparent application for virtual screening of histone deacetylase 1 inhibitors

ABSTRACT Histone deacetylases play an important role in regulating gene expression by modifying histones and changing chromatin conformation. HDAC dysregulation is involved in many diseases, such as cancer, autoimmune and neurodegenerative diseases. Histone deacetylase 1 (HDAC1) inhibitors represent an important class of drugs. Quantitative Structure-Activity Relationship (QSAR) classification models were developed using 2D RDKit molecular descriptors; ECPF4 (Extended Connectivity Fingerprint) circular fingerprints; and the Random Forest, Gradient Boosting, and Support Vector Machine methods. The developed models were integrated into the HDAC1 PREDICTOR application, which is freely available at the link https://ovttiras-hdac1-inhibitors-hdac1-predictor-app-z3mrbr.streamlitapp.com. The HDAC1 PREDICTOR web application allows one to reveal the compounds for which the predicted activity to inhibit HDAC1 is higher than that of the reference Vorinostat compound (IC50 = 11.08 nM). The algorithm implemented in HDAC1 PREDICTOR for determining the contributions of molecular fragments to the inhibitory activity can be used to find the molecule segments that increase or decrease the activity, enabling the researcher to conduct a rational molecular design of new highly active HDAC1 inhibitors. The developed QSAR models and the code for their construction in the Python programming language are freely available on the GitHub platform at https://github.com/ovttiras/HDAC1-inhibitors.

Development of QSAR models to predict blood-brain barrier permeability.

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Predictive modeling of antibacterial activity of ionic liquids by machine learning methods

Structural variation and different bioactivity of ionic liquids (ILs) make them highly promising for the development of novel biocides. Application of computational methods to the evaluation of potential antibacterial activity of chemical compounds is a useful, time- and cost-saving tool replacing numerous experimental syntheses. In the present study, quantitative structure–activity relationship (QSAR) modeling is applied to develop models (based on more than 800 data points) aiming to predict the minimal inhibitory concentration (MIC) of ILs against three types of human pathogens – Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. The random forest model with the AlvaDesc descriptors in general demonstrates the best performance for all the three types of bacteria and is suggested as a final model. To interpret the final model and determine the most significant descriptors, a SHapley Additive exPlanation (SHAP) method was applied. Six amino acid ILs, which were synthesized for the first time, and five halogenide ionic liquids purchased, all based on 1-alkyl-3methylimidozolium cations with different alkyl chain lengths, C10, C12 and C14, are tested in vitro and used to validate the developed QSAR models. The data sets and developed model are available free of charge at http://ochem.eu/article/147386.

Application of QSAR for investigation on coagulation mechanisms of textile wastewater.

Coagulation is an effective preliminary treatment process for textile wastewater. In order to evaluate the effectiveness of the coagulation process, we performed quantitative structure activity relationship (QSAR) analyses with total organic carbon (TOC) removal rates (Rexp) as an index by three different coagulants (AlCl3, FeCl3, and MgCl2). The experimental results showed that the average Rexp of the three coagulants was 39.12%±2.60%, 51.60%±2.88%, and 49.95%±3.17%. Subsequently, 42 molecular descriptors of dye molecules were calculated by quantitative calculation softwares Gaussian 09, Material Studio 7.0, and Multiwfn 3.7, and then QSAR models were constructed by a multiple linear regression (MLR) method for the three coagulation systems. The developed QSAR models demonstrated excellent stability, robustness, and predictability with values of R2 =0.7677, 0.8015, and 0.7035, Q2INT =0.6067, 0.7026, and 0.5898, Q2EXT =0.5505, 0.5076, and 0.5697, respectively. Based on the analysis of quantum parameters, the coagulation mechanism for AlCl3, FeCl3 is primarily electrostatic adsorption as well as hydrogen bonding, while MgCl2 coagulates dyes mainly by electrostatic adsorption. This study provides an assessment of the removal rules and a feasible method for predicting dye removal rates in AlCl3, FeCl3, and MgCl2 coagulation process.

Development of new QSAR models for water, sediment, and soil half-life.

Checking the persistence of a chemical in the environment is extremely important. Regulations like REACH, the European one on chemicals, require the measurements or estimates of the half-life of the chemical in water, sediment, and soil. The use of non-testing methods, like quantitative structure-activity relationship (QSAR) models, is encouraged because it reduces costs and time. To our knowledge, there are very few freely available models for these properties and some are for specific chemical classes. Here, we present three new semi-quantitative models, one for each of the required environmental compartments (water, sediment, and soil). Using literature and REACH registration data, we developed three new counter-propagation artificial neural network models using the CPANNatNIC tool. We calculated the VEGA descriptors, and selected the relevant ones using an internal method in R based on the forward selection technique. The best model for each compartment was implemented in two open-source stand-alone tools, the VEGA platform, and the JANUS tool (https://www.vegahub.eu/). These models were also used by ECHA to build their PBT profiler available in the OECD QSAR toolbox (https://qsartoolbox.org/). Screening and prioritization are also our main target. The models perform well, with R2 always above 0.8 in training and validation. The only exception is the validation set of the soil compartment, with R2 0.68, that is above 0.8 only for compounds inside the applicability domain (automatically calculated by the system). The root mean square error (RMSE) is good, 0.34 or less in log units (again, for soil validation it is higher but it reaches 0.21 when considering only the compounds in the applicability domain). Compared with one of the most widely used tools, BIOWIN3, the proposed models give better results in terms of R2 and RMSE. For the classification, the performance is better for water and soil, and comparable or lower for sediment.

Development of QSAR model using machine learning and molecular docking study of polyphenol derivatives against obesity as pancreatic lipase inhibitor

In developed countries and developing countries, obesity/overweight is considered a major problem, in fact, it is now recognized as a major metabolic disorder. Additionally, obesity is connected with other metabolic diseases, including cardiovascular disorders, type 2 diabetes, some types of cancer, etc. Therefore, the development of novel drugs/medications for obesity is essential. The best target for treating obesity is Pancreatic Lipase (PL), it breaks 50-70% triglycerides into monoglycerol and free fatty acids.The major aim of this in silico study is to generate a QSAR model by using Multiple Linear Regression (MLR) and to inhibit pancreatic lipase by polyphenol derivatives mainly flavonoids, plant secondary metabolites shows good inhibitory activity against PL, maybe with less unpleasant side effects.In this in silico study, a potent inhibitor was found through calculating drug likness, QSAR (Quantitative structure-activity relationship) and molecular docking. The docking was performed in Maestro 12.0 and the ADME (absorption, distribution, metabolism, and excretion) properties (drug-likeness) of compounds/ligands were predicted by the Qikprop module of Maestro 12.0. The QSAR model was developed to show the relationship between the chemical/structural properties and the compound's biological activity. We have found the best interaction between pancreatic lipase and flavonoids. The best docked compound is Epigallocatechin 3,5,-di-O-gallate with docking score −10.935 kcal/mol .All compounds also show drug-likeness activity.The developed model has satisfied all internal and external validation criteria and has square correlation coefficient (r2) 0.8649, which shows its predictive ability and has good acceptability, predictive ability, and statistical robustness. Communicated by Ramaswamy H. Sarma

Development of QSAR Model of Caffeic Acid Phenethyl Ester as Anti-Cancer HT-29

Caffeic Acid Phenethyl Ester (CAPE) compounds are potentially colorectal anticancer drugs. QSAR (Quantitative Structure-Activity Relationship) research on the CAPE compound class has been carried out, but the model in the previous study did not meet the goodness of fit criteria. The development of the CAPE compound QSAR model as a colorectal anticancer was carried out to obtain a model that meets the goodness of fit criteria and is valid. Eighteen CAPE compounds were used to build the QSAR model using the Multiple Linear Regression (MLR) technique. The descriptor selection was carried out using the backward elimination method and the validation test using the internal leave one out (LOO) cross-validation. The results showed that the QSAR model with four descriptors, namely MDEC22, MDEC23, JGI1, and molecular weight (BM), met the goodness of fit and Q2(LOO) criteria. The development of the QSAR model by adding the LogP descriptor resulted in the QSAR 5 descriptor model with higher goodness of fit level than the QSAR model without the LogP descriptor. Both of these QSAR models have the potential to be used as predictors in the development of a new class of CAPE compounds as anticancer agents against HT-29 cells.

Prediction of aquatic toxicity of energetic materials using genetic function approximation

• Prediction of aquatic toxicity of nitrogen-rich energetic materials is reported. • A sophisticated regression method, namely, genetic function approximation was used. • The obtained QSPR models provide good correlation ( R 2 = 0.81-0.82) for various exposure times. • The models were tested for various families of energetic materials and experimental conditions. The first attempt to use genetic function approximation (GFA) for prediction of aquatic toxicity of soluble energetic materials is reported in this paper. The prediction is based on the estimation of the luminescent bacteria Aliivibrio fischeri inhibition in water according to the recently reported experimental results. Thus, two quantitative structure-activity relationship (QSAR) models for 15 and 30 min exposure were obtained, which include five and six essential descriptors, respectively. Most of them are so-called “fast descriptors” assuming there is no need for quantum-chemical calculations. The rest descriptors are obtained in terms of semi-empirical approach allowing the prediction to be rapidly complete. The developed QSAR models provide relatively high correlation coefficients, namely, R 2 = 0.81 and 0.82 for 15 min and 30 min datasets, respectively. The experimental datasets included a number of values, which were presented ambiguously (< or > than certain values). Thus, these have not been included (13 for 15 min and 10 for 30 min datasets) in the training sets and used them as the corresponding test sets. As a result, the developed models accurately indicate what exactly the higher and lower values should be applied instead of ones presented with ambiguity. Thus, the results may be useful for predicting the aquatic toxicity of new nitrogen-rich energetic materials, both molecular and ionic, bearing nitro, nitramino, azido groups and other commonly used explosophores.

First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability.

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.

Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica

Chemicals used in our daily life show different toxic effects to the aquatic and terrestrial species and thus hamper the ecological balance. In the present time, amphibians are one of them, which are threatened to be extinct. Quantitative structure-activity relationship (QSAR) is an useful tool for prediction involving less time, money and manpower without requiring any animal experiments to assess the unavailable acute toxicity data for the untested molecules. In this study, we have developed QSAR models for ecotoxicity of some waterborne diverse aromatic compounds on an amphibian species Rana japonica (Japanese brown frog) employing Genetic Algorithm (GA) for variable selection followed by Partial Least Squares (PLS) regression method following recommendations of the Organization for Economic Co-operation and Development (OECD) for QSAR model development. Double cross-validation (DCV) followed by Best Subset Selection (BSS) were employed to select suitable models. The models displayed promising statistical quality in terms of R2 (= 0.837–0.841), Q2LOO (= 0.782–0.787), R2pred or Q2F1 (= 0.802–0.82) and some other internal and external validation metrics for tadpoles of Rana japonica (NTraining = 44, NTest = 14). These models can be applied for data gap filling for a new untested compound falling within the applicability domain (AD) of the models.

Unveil the quantum chemical descriptors determining direct photodegradation of antibiotics under simulated sunlight: Batch experiments and model development

The massive use of antibiotics has led to their omnipresence in aquatic environments, and the photodegradation was found to be the dominant transformation process for antibiotics in the natural river system. Herein, we investigated the photodegradation kinetics of 77 antibiotics in 7 classes in water under simulated sunlight. Using the quantum chemical descriptors predicted by the density functional theory calculation, the quantitative structure-activity relationship (QSAR) models were established to explore the main chemical descriptors determining the photodegradation of antibiotics. The results showed that the photodegradation kinetics of antibiotics conformed to the pseudo-first order kinetic model. The photodegradation rate constants of different antibiotics varied 4 orders of magnitude, and the photodegradation rate constants of quinolones were significantly higher than those for other classes of antibiotics due to the F atoms in their molecular structures. The developed QSAR models revealed that the energy gap (Egap) between ELUMO and EHOMO was the main chemical descriptor determining the photodegradation of antibiotics, and it was negatively correlated with lgk. In addition, the number of F atom was also included in the QSAR models due to the great contribution of F atom to the direct photolysis of quinolones. This study ordered the photodegradation rate constants of 77 antibiotics, and revealed the major chemical descriptors determining the photodegradation of antibiotics. The results provide the basic information for the photolysis of antibiotics, which is significant for predicting the environmental behaviors and evaluating the ecological risks of antibiotics in aquatic environments.