QSAR models using a large diverse set of estrogens

Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health

Cabbage is a widely consumed vegetable in the human diet because of its low cost, broad availability and high nutritional value. The rising use of pesticides in food production creates a need to assess vegetable toxicity, which primarily results from residues in food products and environmental exposure. The study aims to offer exploration of vegetable toxicity in cabbage with the help of reliable QSTR and q-RASTR models. All the developed models were robust and predictive enough (Q2LOO = 0.7491-0.8164, Q2F1 = 0.5243-0.6253, Q2F2 = 0.513-0.617, MAEext = 0.495-0.690). Furthermore, the reliability and predictability of models were assessed and confirmed by applicability domain and prediction reliability indicator analysis. Additionally, different machine learning models were developed to making effective predictions and multiple linear regression (MLR) comparison. Consensus approach was advocated data gap filling for USEPA ECOTOX database compounds. The most and least toxic compounds from both MLR model predictions were prioritized and analyzed. Mechanistic interpretation highlighted the structural features or fragments responsible for the agrochemical toxicity and a safe approach for designing green chemicals minimizing the toxicity. This first reported study can be useful for toxicity profiling, data gap filling and designing safer and green agrochemical for minimizing vegetable toxicity, healthy human life and environmental safety.

Intelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210

Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process. In the present study, we developed predictive Quantitative Structure-Activity Relationship (QSAR) models to assess chronic effects of chemicals on ELS in fish. Toxicity data for ELS in fish was collected from two different sources, i.e. J-CHECK and eChemPortal, which contain robust study summaries of experimental studies performed according to OECD Test Guideline 210. The collected data included two types of endpoints - the No Observed Effect Concentration (NOEC) and the Lowest Observed Effect Concentration (LOEC), which were utilized to develop the QSAR models. Six different partial least squares (PLS) models with various descriptor combinations were created for both endpoints. These models were then employed for intelligent consensus-based prediction to enhance predictability for unknown chemicals. Among these models, the consensus model - 3 (Q2F1 = 0.71, Q2F2 = 0.71) and individual model - 3 (Q2F1 = 0.80, Q2F2 = 0.79) exhibited most promising results for both the NOEC and LOEC endpoints. Furthermore, these models were validated experimentally using experimental data from nine different industrial chemicals provided by Global Product Compliance (Europe) AB. Lastly, the models were used to screen and prioritize chemicals obtained from the Pesticide Properties (PPDB) and DrugBank databases.

A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)

Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. A total of 36 single ML models and 12 consensus models were developed. The results indicated that models employing concentration addition (CA), independent action (IA), and molecular descriptors (MD) as variables demonstrated superior predictive abilities. The consensus model combining SVM and RF algorithms (labeled as CM0) demonstrated the highest level of accuracy in fitting the data, with a coefficient of determination of 0.980. Additionally, it showed strong predictive abilities when tested with external data, achieving an external R2 value of 0.945 and a Concordance Correlation Coefficient of 0.967. This study provides a positive contribution to the ecological risk assessment of a mixture of azole fungicides.

QSAR models for predicting key hormesis parameters of quinolone antibiotic mixtures on Vibrio qinghaiensis sp.-Q67

Antibiotics, as emerging pollutants, are increasingly detected in various water bodies at low-doses. The hormesis effect observed at these low-doses presents a challenge for toxicity prediction. Accurately predicting the key parameters of the hormesis effect is crucial. However, current methods for predicting the key parameters of hormesis mixtures (ECmin and ZEP) are limited. This study introduces machine learning-based QSAR (quantitative structure-activity relationship) models designed to predict these parameters. We conducted a binary mixture toxicity experiment using 10 quinolone antibiotics, with Q67 as the indicator organism, to obtain experimental data. Molecular structure descriptors of the antibiotics were calculated, and the optimal descriptors were selected. Additionally, molecular docking was used to convert the relative 3D conformation of antibiotic-protein complexes into SMILES strings. QSAR models were developed using the GA-MLR (genetic algorithms multivariate linear regression) method and the Transformer-CNN (Transformer model and convolutional neural network) method with the mixture descriptors and SMILES strings as independent variables and the toxic effect values (EC50, ECmin, and ZEP) as dependent variables. The models were validated internally and externally, demonstrating reliable prediction of the toxic effect values of EC50, ECmin, and ZEP at three different exposure times (4 h, 12 h, and 24 h), model quality is better with longer exposure times. The QSAR model exhibited strong internal stability and external predictive ability. A comparison of the two modelling approaches showed that the Transformer-CNN method produced QSAR models with a coefficient of determination (R2) ranging from 0.8458 to 0.9853, and a root mean square error (RMSE) ranging from 0.0409 to 0.1496, indicating higher accuracy in predicting time-dependent toxicity. This study offers a novel approach to exploring and predicting the key parameters of the hormesis effect.

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies

Due to the high toxicity, poor efficacy and resistance associated with current anti-breast cancer drugs, there's growing interest in natural products (NPs) for their potential anti-cancer properties. Computational modelling of NPs to identify key structural features can aid in developing novel natural inhibitors. In this study, we developed statistically significant QSAR models based on NPs from the NPACT database, which have shown potential anticancer activity against the MCF-7 cancer cell lines. All the developed QSAR models were statistically robust, meeting both internal (R 2  = 0.666-0.669, R 2 adj  = 0.657-0.660, Q 2 Loo  = 0.636-0.638) and external (Q 2 F n  = 0.686-0.714, CCC ext = 0.830-0.847) validation criteria. Consequently, they were utilized to virtually screen a series of NPs from the COCONUT database in the search for novel natural inhibitors. Molecular docking studies were conducted on the identified compounds against the human HER2 protein (PDB ID: 3PP0), which is a crucial target in breast cancer. Molecular docking analysis demonstrated that compounds 4608 and 2710 achieved the highest docking scores, with CDOCKER interaction energies of -72.67 kcal/mol and - 72.63 kcal/mol respectively. Compounds 4608 and 2710 were identified as the most promising candidates upon performing triplicate 100 ns MD simulation study using the CHARMM36 force field. DFT studies was performed to evaluate their stability and reactivity as potential drug molecules. This research contributes to the development of new natural inhibitors for breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00266-5.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R2), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R2 and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R2) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R2) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R2) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors

The 3C-like protease (3CL^pro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CL^pro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CL^pro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CL^pro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CL^pro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CL^pro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

A QSAR study to predict the survival motor neuron promoter activity of candidate diaminoquinazoline derivatives for the potential treatment of spinal muscular atrophy

Spinal Muscular Atrophy is a genetic neuromuscular disease that leads to muscle weakness and atrophy and it is characterized by the loss of α-motor neurons in the spinal cord's anterior horn cells. The disease appears due to low levels of the survival motor neuron protein. There are continuing clinical trials for the treatment of Spinal Muscular Atrophy. Quinazoline-based compounds are promising since they were tested on fibroblasts derived from the patients and found to increase the survival motor neuron protein levels. In this study, using multiple linear regression, we generated robust and valid quantitative structure- activity relationship models to predict the survival motor neuron-2 promoter activity of the new candidate compounds using the experimental survival motor neuron-2 promoter activity values of 2,4-diaminoquinazoline derivatives taken from the literature. The novel compounds designed by combining the pyrido[1,2-α]pyrimidin-4-one moeity of the known drug Risdiplam with that of 2,4 - diaminoquinazoline scaffold were predicted to exhibit strong promoter activities.

In silico selectivity modeling of pyridine and pyrimidine based CYP11B1 and CYP11B2 inhibitors: A case study

DESIGN

of selective drug candidates for highly structural similar targets is a challenging task for researchers. The main objective of this study was to explore the selectivity modeling of pyridine and pyrimidine scaffold towards the highly homologous targets CYP11B1 and CYP11B2 enzymes by in silico (Molecular docking and QSAR) approaches. In this regard, a big dataset (n = 228) of CYP11B1 and CYP11B2 inhibitors were gathered and classified based on heterocyclic ring and the exhaustive analysis was carried out for pyridine and pyrimidinescaffolds. The LibDock algorithm was used to explore the binding pattern, screening, and identify the structural feature responsible for the selectivity of the ligands towards the studied targets. Finally, QSAR analysis was done to explore the correlation between various binding parameters and structural features responsible for the inhibitory activity and selectivity of the ligands in a quantitative way. The docking and QSAR analysis clearly revealed and distinguished the importance of structural features, functional groups attached for CYP11B2 and CYP11B1 selectivity for pyridine and pyrimidine analogs. Additionally, the docking analysis highlighted the differentiating amino acids residues for selectivity for ligands for each of the enzymes. The results obtained from this research work will be helpful in designing the selective CYP11B1/CYP11B2 inhibitors.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors

COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (M^pro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model’s applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.

Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists

Toll-like receptor 7 (TLR7) is activated in response to the binding of single-stranded RNA. Its over-activation has been implicated in several autoimmune disorders, and thus, it is an established therapeutic target in such circumstances. TLR7 small-molecule antagonists are not yet available for therapeutic use. We conducted a ligand-based drug design of new TLR7 antagonists through a concerted effort encompassing 2D-QSAR, 3D-QSAR, and pharmacophore modelling of 54 reported TLR7 antagonists. The developed 2D-QSAR model depicted an excellent correlation coefficient (R²_training: 0.86 and R²_test: 0.78) between the experimental and estimated activities. The ligand-based drug design approach utilizing the 3D-QSAR model (R²_training: 0.95 and R²_test: 0.84) demonstrated a significant contribution of electrostatic potential and steric fields towards the TLR7 antagonism. This consolidated approach, along with a pharmacophore model with high correlation (R_training: 0.94 and R_test: 0.92), was used to design quinazoline-core-based hTLR7 antagonists. Subsequently, the newly designed molecules were subjected to molecular docking onto the previously proposed binding model and a molecular dynamics study for a better understanding of their binding pattern. The toxicity profiles and drug-likeness characteristics of the designed compounds were evaluated with in silico ADMET predictions. This ligand-based study contributes towards a better understanding of lead optimization and the future development of potent TLR7 antagonists.

Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity: QSAR modeling using quantum-chemical descriptors

Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q² _ext and Q² _cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC₅₀ and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC₅₀) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r²) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC₅₀ values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC₅₀ values were predicted based on the average prediction of ten models.

Concentration Addition, Independent Action, and Quantitative Structure-Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata

The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pK_a ). The toxicities of synergetic mixtures can be interpreted with the total energy (E_T ) and pK_a of the mixtures. Dipole moment and E_T are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.

Predictive hybrid paradigm for cytotoxic activity of 1,3,4-thiadiazole derivatives as CDK6 inhibitors against human (MCF-7) breast cancer cell line and its structural modifications: rational for novel cancer therapeutics

The dysregulation of cyclin-CDK6 interactions has been implicated in human breast cancer, providing a rationale for more therapeutic options. Recently, ATP-competitive inhibitors have been employed for managing breast cancer. These molecules, like most natural CDKs inhibitors, potently bind in the ATP-binding site of CDK6 to regulate trans-activation. Nonetheless, only a few numbers of these molecules are approved to mitigate breast cancer, thus, ensuring that the search for more selective inhibitors continues. In this study, we attempted to establish the selective predictive models for identifying potent CDK6 inhibitors against a human breast cancer cell-line using a dataset of fifty-two 1,3,4-thiadiazole derivatives. The significant eight descriptor hybrid QSAR models generated exhibited encouraging statistical attributes including R2> 0.70, Q²_LOO > 0.70, Q²_LMO > 0.60, Qfn2 > 0.6. Furthermore, the study designed new compounds based on the activity and structural basis for selectivity of compounds for CDK6. While demonstrating good potency and modest selectivity, the compound C16, which showed significantly high activity of 5.5607 µM and binding energy value of -9.0 Kcal/mol, was used as template for compounds design to generate 10 novel series of 1,3,4-thiadiazole analogues containing benzisoselenazolone scaffolds, with significant pharmacological activity and better selectivity for CDK6. By our rationale, four of the designed compounds (C16b, C16h, C16i, and C16j) with activity values of 6.2584 µM, 6.7812 µM, 6.4717 µM, and 6.2666 µM respectively, and binding affinities of -10.0 kcal/mol, -9.9 kcal/mol, -9.9 kcal/mol, and -9.9 kcal/mol respectively, may emerge as therapeutic options for breast cancer treatment after extensive in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists

We performed a structural study followed by theoretical analysis of the chemical descriptors and biological activity of a series of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 (CB1) receptor antagonists.

In silico design and analysis of NS4B inhibitors against hepatitis C virus

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

Combined QSAR Model and Chemical Similarity Search for Novel HMG-CoA Reductase Inhibitors for Coronary Heart Disease

Background:

Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming.

Methods:

In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search.

Results and Discussion:

The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold.

Conclusion:

The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.

MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling

SUMMARY

Quantitative structure-activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs.

AVAILABILITY AND IMPLEMENTATION

The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Principles of QSAR Modeling

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

Background: In the recent years, histamine H3 receptor (H3R) has been receiving increasing attention in pharmacotherapy of neurological disorders. The aim of the current study was to investigate structural requirements for the prediction of H3 antagonistic activity using quantitative structure-activity relationship (QSAR) and molecular docking techniques. Methods: To this end, genetic algorithm coupled partial least square and stepwise multiple linear regression methods were employed for developing a QSAR model. The obtained QSAR model was stringently assessed using different validation criteria. Results: The generated model indicated that connectivity information and mean absolute charge are two important descriptors for the prediction of H3 antagonistic activity of the studied compounds. To gain insight into the mechanism of interaction between studied molecules and H3R, molecular docking was performed. The most important residues involved in the ligand-receptor interactions were identified. Conclusion: The result of current study can be used for designing of new H3 antagonist and proposing structural modifications to improve H3 inhibitory potency.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment. The generated models have good fitting performances (R²: 0.75-0.99), they are internally robust (Q²loo: 0.66-0.98) and can handle up to 30% of perturbation of the training set (Q² lmo: 0.64-0.98). The absence of chance correlation was guaranteed by low values of R² calculated on scrambled responses (R² Y_scr: 0.11-0.38). Different statistical parameters were used to quantify the external predictivity of the models (CCC_ext: 0.73-0.91, Q² ext-Fn: 0.53-0.96). The results indicate that all the best models are predictive when applied to chemicals not involved in the models development. In addition, all models have similar accuracy both in fitting and in prediction and this represents a good degree of generalization. These models may be useful to support the risk assessment procedure when experimental data for key species are missing or to create prioritization lists for the general a priori assessment of the potential toxicity of existing and new pesticides which fall in the applicability domain.

Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model

The diffusion coefficient (D) is a key physicochemical parameter for the diffusive gradients in thin films technique (DGT) for environmental sampling, which has been extended to organic chemicals (so called o-DGT). D can be measured in the laboratory, although for organic chemicals this parameter might be predicted based on chemical structure. Here we developed for the first time a Quantitative Structure-Property Relationship (QSPR) model to predict the D values. Twenty quantum chemical descriptors that quantify the electronic and energy properties of 120 organic compounds were selected together with molecular mass, solubility and hydrophobicity. The best QSPR model was established by using genetic algorithm and multiple linear regression (GA-MLR). The results indicated that the model derived from the average molecular polarizability (α), the chemical potential (ξ) and the global electrophilicity index (ω) could explain the diffusion of organics in o-DGT and had good statistical performance (R² = 0.767, RMSE = 0.101). Different validation strategies confirmed that the developed model was robust and predictive. 93% of tested compounds were within the applicability domain (AD) and predicted accurately. We concluded that the proposed QSPR model can serve as an efficient predictive tool for new chemicals in the AD, would be useful to cross validate measured D values and provide a better the understanding of the diffusive behaviour of organics in o-DGT and measurements in the environment. It might also be useful in the non-target analysis with o-DGT for chemicals without measured D values.

QSAR studies on benzothiophene derivatives as Plasmodium falciparum N-myristoyltransferase inhibitors: Molecular insights into affinity and selectivity

Malaria is an infectious disease caused by protozoan parasites of the genus Plasmodium and transmitted by Anopheles spp. mosquitos. Due to the emerging resistance to currently available drugs, great efforts must be invested in discovering new molecular targets and drugs. N-myristoyltransferase (NMT) is an essential enzyme to parasites and has been validated as a chemically tractable target for the discovery of new drug candidates against malaria. In this work, 2D and 3D quantitative structure-activity relationship (QSAR) studies were conducted on a series of benzothiophene derivatives as P. falciparum NMT (PfNMT) and human NMT (HsNMT) inhibitors to shed light on the molecular requirements for inhibitor affinity and selectivity. A combination of Quantitative Structure-activity Relationship (QSAR) methods, including the hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) models, were used, and the impacts of the molecular alignment strategies (maximum common substructure and flexible ligand alignment) and atomic partial charge methods (Gasteiger-Hückel, MMFF94, AM1-BCC, CHELPG, and Mulliken) on the quality and reliability of the models were assessed. The best models exhibited internal consistency and could reasonably predict the inhibitory activity against both PfNMT (HQSAR: q² /r² /r² _pred = 0.83/0.98/0.81; CoMFA: q² /r² /r² _pred = 0.78/0.97/0.86; CoMSIA: q² /r² /r² _pred = 0.74/0.95/0.82) and HsNMT (HQSAR: q² /r² /r² _pred = 0.79/0.93/0.74; CoMFA: q² /r² /r² _pred = 0.82/0.98/0.60; CoMSIA: q² /r² /r² _pred = 0.62/0.95/0.56). The results enabled the identification of the polar interactions (electrostatic and hydrogen-bonding properties) as the major molecular features that affected the inhibitory activity and selectivity. These findings should be useful for the design of PfNMT inhibitors with high affinities and selectivities as antimalarial lead candidates.

Norm index for predicting the rate constants of organic contaminants oxygenated with sulfate radical

The degradation of organic contaminants in aquatic systems has raised immense attention worldwide, and the second-order rate constant ([Formula: see text]) of water pollutants oxidized by sulfate radical anion is an important index for assessing the degradation efficiency of organics. Herein, a new norm mathematical formula is defined. Based on this, four new descriptors are proposed and a QSPR model is developed for predicting [Formula: see text] using 30 families of emerging organic pollutants in water. The statistical results fully prove that this model has good fitting effect and stability with R² of 0.8862, Q²_LOO of 0.8466, and Q²_5-fold of 0.8329, respectively. The validation results including cross validation, applicability domain analysis, and model comparison show that this model has good robustness, predictive performance, and reliability. These decent results indicate that the new norm mathematical formula is effective in calculating descriptors and the norm indexes have a great application for evaluating the transformation fate of organic pollutants by sulfate radical in aquatic systems.

Investigation on Quantitative Structure-Activity Relationships of 1,3,4-Oxadiazole Derivatives as Potential Telomerase Inhibitors

BACKGROUND

Telomerase, a reverse transcriptase, maintains telomere and chromosomes integrity of dividing cells, while it is inactivated in most somatic cells. In tumor cells, telomerase is highly activated, and works in order to maintain the length of telomeres causing immortality, hence it could be considered as a potential marker to tumorigenesis.A series of 1,3,4-oxadiazole derivatives showed significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition.

METHODS

This series of 24 N-benzylidene-2-((5-(pyridine-4-yl)-1,3,4-oxadiazol-2yl)thio)acetohydrazide derivatives as telomerase inhibitors has been considered to carry out QSAR studies. The endpoint to build QSAR models is determined by the IC50 values for telomerase inhibition, i.e., the concentration (μM) of inhibitor that produces 50% inhibition. These values were converted to pIC50 (- log IC50) values. We used the most common and transparent method, where models are described by clearly expressed mathematical equations: Multiple Linear Regression (MLR) by Ordinary Least Squares (OLS).

RESULTS

Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds.

CONCLUSION

The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.

Development of predictive models for silicone rubber-water partition coefficients of hydrophobic organic contaminants

The silicone rubber passive sampling technique is extensively applied to monitor the aqueous freely dissolved concentration of hydrophobic organic compounds (HOCs). The silicone rubber-water partition coefficient (Ksrw) is an important parameter to accurately measure the concentrations of chemicals using passive sampling devices. In this study, two theoretical linear solvation energy relationship (TLSER) models and a quantitative structure-property relationship (QSPR) model were developed for predicting the Ksrw of HOCs. The 119 model compounds studied here included 31 personal care products, such as musks, UV-filters, and organophosphate flame retardants, as well as "conventional" pollutants, such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls. The statistical parameters indicated that the final QSPR model with seven descriptors for all 119 chemicals had a satisfactory goodness-of-fit (Radj2 = 0.898), robustness (QLOO2 = 0.881) and predictive ability (Qext-F1,2,32 = 0.897-0.926). In comparison, the results of one TLSER model with four descriptors for 113 chemicals (Radj2 = 0.826, QLOO2 = 0.790, Qext-F1,2,32 = 0.805-0.837) and another TLSER model with one descriptor for 5 chemicals (Radj2 = 0.747, QLOO2 = 0.647) were also acceptable. The applicability domains of the obtained models covered chemicals containing hydroxyl, imino groups, carbonyl groups, ether bonds, halogen atoms, sulfur atoms, phosphorus atoms, nitro groups, and cyano groups. In addition, the structural features governing the partition behavior of chemicals between silicone rubber and water were explored through interpretation of appropriate mechanisms.

A Comprehensive QSAR Study on Antileishmanial and Antitrypanosomal Cinnamate Ester Analogues

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds' anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities.

Norm index-based QSTR model to predict the eco-toxicity of ionic liquids towards Leukemia rat cell line

The evaluation of eco-toxicity of ionic liquids (ILs) in the aquatic environment is essential for their safe utilization and QSTR approach plays an important role in obtaining the eco-toxicity data of ILs with diverse structures. Usually, the descriptors used to build QSTR model were made up of anion and cation descriptors, and their interactions were often neglected to some extent. In this work, based on the optimization of the ILs structure, a new set of descriptors were proposed to describe the interaction between anions and cations, and some new atomic distribution matrices were constructed to calculate norm descriptors of ILs, anion and cation. A norm index-based QSTR model was built to predict the eco-toxicity of ILs toward Leukemia rat cell line (IPC-81). This model has satisfactory statistical results with the R² of 0.954 and RMSE of 0.241, respectively. Furthermore, leave-one-out cross-validation and applicability domain results showed good stability and predictability of this model. This approach showed that the interaction between cations and anions could be reflected by optimizing the whole structure of ILs which might play an important role for describing the eco-toxicity of ILs. Therefore, it is further suggested that the norm descriptors would be applicable to predict the eco-toxicity of ILs towards IPC-81.

In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r² = 0.8129, CCC = 0.8979 and Q² = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.

3D-QSAR and Molecular Docking Studies on Oxadiazole Substituted Benzimidazole Derivatives: Validation of Experimental Inhibitory Potencies Towards COX-2

Background:

In past few decades, computational chemistry has seen significant advancements in design and development of novel therapeutics. Benzimidazole derivatives showed promising anti-inflammatory activity through the inhibition of COX-2 enzyme.

Objective:

The structural features necessary for COX-2 inhibitory activity for a series of oxadiazole substituted benzimidazoles were explored through 3D-QSAR, combinatorial library generation (Combi Lab) and molecular docking.

Methods:

3D-QSAR (using kNN-MFA (SW-FB) and PLSR (GA) methods) and Combi Lab studies were performed by using VLife MDS Molecular Design Suite. The molecular docking study was performed by using AutoDockVina.

Results:

Significant QSAR models generated by PLSR exhibited r2 = 0.79, q2 = 0.68 and pred_r2 = 0. 84 values whereas kNN showed q2 = 0.71 and pred_r2 = 0.84. External validation of developed models by various parameters assures their reliability and predictive efficacy. A library of 72 compounds was generated by combinatorial technique in which 11 compounds (A1-A5 and B1-B6) showed better predicted biological activity than the most active compound 27 (pIC50 = 7.22) from the dataset. These compounds showed proximal interaction with amino acid residues like TYR355 and/or ARG120 on COX-2(PDB ID: 4RS0).

Conclusion:

The present work resulted in the design of more potent benzimidazoles as COX-2 inhibitors with good interaction as compared to reference ligand. The results of the study may be helpful in the development of novel COX-2 inhibitors for inflammatory disorders.

An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Background

Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural properties of compounds and phenotypic effects of an exposure.

Results

Here, we propose a computational strategy that integrates molecular descriptors and MOA information to better explain the mechanisms underlying biological endpoints of interest. By applying our methodology, we obtained a statistically robust and validated model to predict the binding affinity to human serum albumin. Our model is also able to provide new venues for the interpretation of the chemical-biological interactions.

Conclusion

Our observations suggest that integrated quantitative models of structural and MOA-activity relationships are promising complementary tools in the arsenal of strategies aiming at developing new safe- and useful-by-design compounds.

Electronic supplementary material

The online version of this article (10.1186/s13321-019-0359-2) contains supplementary material, which is available to authorized users.