Showing papers in "Sar and Qsar in Environmental Research in 2020"

Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases.

Abstract: In the context of recently emerged pandemic of COVID-19, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) modelling using SARS-CoV-3CLpro enzyme inhibitors for the development of a multiple linear regression (MLR) based model. We have used 2D descriptors with an aim to develop an easily interpretable, transferable and reproducible model which may be used for quick prediction of SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process. Based on the insights obtained from the developed 2D-QSAR model, we have identified the structural features responsible for the enhancement of the inhibitory activity against 3CLpro enzyme. Moreover, we have performed the molecular docking analysis using the most and least active molecules from the dataset to understand the molecular interactions involved in binding, and the results were then correlated with the essential structural features obtained from the 2D-QSAR model. Additionally, we have performed in silico predictions of SARS-CoV 3CLpro enzyme inhibitory activity of a total of 50,437 compounds obtained from two anti-viral drug databases (CAS COVID-19 antiviral candidate compound database and another recently reported list of prioritized compounds from the ZINC15 database) using the developed model and provided prioritized compounds for experimental detection of their performance for SARS-CoV 3CLpro enzyme inhibition.

Weighted Mostar indices as measures of molecular peripheral shapes with applications to graphene, graphyne and graphdiyne nanoribbons.

Abstract: In this study we consider relatively new bond-additive Mostar indices that appear to provide quantitative measures of peripheral shapes of molecules. We have computed weighted Mostar, edge-Mostar and total-Mostar indices of graphene, [Formula: see text]-types of graphyne and graphdiyne, which are of considerable interest owing to their novel properties and thus find applications in a number of areas such as sensors, catalysis, chemisorption and nanomedicine. We have implemented the results to analyse the weighted Mostar indices and have obtained exact analytical expressions for the title molecules. We propose that Mostar indices together with frontier molecular orbitals, and HOMO-LUMO gaps can provide measures of chemical reactivity and analysis of peripheral molecular shapes.

Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues.

Abstract: A set of 23 steroidal 1,2,4,5-tetraoxane analogues were studied using quantum-chemical method (B3LYP/6-31 G*) and multivariate analyses (PCA, HCA, KNN and SIMCA) in order to calculate the propertie...

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation.

Abstract: Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because ...

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling.

Abstract: A quantitative structure–activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden ...

A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants.

Abstract: The Monte Carlo algorithm was applied to formulate a robust quantitative structure–property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data ...

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation.

Abstract: In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on qu...

Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines.

Abstract: Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation.

Abstract: Aedes aegypti is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly...

QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity

Abstract: Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC50) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r2 = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r2 = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r2 = 0.74 and 0.75 for the CORAL model).

Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish.

Abstract: We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSEext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.

Exploring 2D-QSAR for prediction of beta-secretase 1 (BACE1) inhibitory activity against Alzheimer’s disease

Abstract: We have developed a robust quantitative structure-activity relationship (QSAR) model employing a dataset of 98 heterocycle compounds to identify structural features responsible for BACE1 (beta-secretase 1) enzyme inhibition. We have used only 2D descriptors for model development purpose thus avoiding the conformational complications arising due to 3D geometry considerations. Following the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have developed models using stepwise regression analysis followed by the best subset selection, while the final model was developed by partial least squares regression technique. The model was validated using various internationally accepted stringent validation parameters. From the insights obtained from the developed model, we have concluded that heteroatoms (nitrogen, oxygen, etc.) present within to an aromatic nucleus and the structural features such as hydrophobic, ring aromatic and hydrogen bond acceptor/donor are responsible for the enhancement of the BACE1 enzyme inhibitory activity. Moreover, we have performed the pharmacophore modelling to unveil the structural requirements for the inhibitory activity against the BACE1 enzyme. Furthermore, molecular docking studies were carried out to understand the molecular interactions involved in binding, and the results are then correlated with the requisite structural features obtained from the QSAR and pharmacophore models.

Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach.

Abstract: Interleukin (IL)-33 is a new cytokine of the IL-1 family that is related to several inflammatory and autoimmune diseases. IL-33 binds to its ST2 receptor and leads to biological responses thereof. Currently, no drugs have been approved for the treatment of IL-33 related diseases. The aim of this study was to search for small molecules that inhibit the protein-protein interaction between IL-33 and ST2. A virtual screening was first performed to identify potential molecules that can bind IL-33. By analysing the interactions between key residues in the complex of IL-33/ST2, two pharmacophore hypotheses were then generated based on the 'mimicry' and 'pair-rule' principles. From a database of 62,074 compounds, 60 molecules satisfying the pharmacophore models were identified and docked to IL-33. Among 35 compounds successfully docked into the protein, 9 potential ligands in complex with IL-33 were selected for further analysis by molecular dynamics simulations. Based on the stability of the complexes and the interactions of each ligand with the key residues of IL-33, two compounds DB00158 and DB00642 were identified as the most potential inhibitors that can be further investigated as promising novel IL-33 inhibitory drugs.

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

Abstract: High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.

Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent.

Abstract: Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be use...

Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses

Abstract: Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G towards BRD2 over BRD4. The root-mean-square fluctuation (RMSF) analysis indicates that the structural flexibility of BRD4 is stronger than that of BRD2. Moreover the calculated distances between the Cα atoms in the centres of the ZA_loop and BC_loop of BRD4 are also bigger than that of BRD2. The rank of binding free energies calculated using MM-GBSA method agrees well with that determined by experimental data. The results show that 87D can bind more favourably to BRD2 than BRD4, while 88M has better selectivity on BRD4 over BRD2. Residue-based free-energy decomposition method was utilized to estimate the inhibitor-residue interaction spectrum and the results not only identify the hot interaction spots of inhibitors with BRD2 and BRD4, but also demonstrate that several common residues, including (W370, W374), (P371, P375), (V376, V380) and (L381, L385) belonging to (BRD2, BRD4), generate significant binding difference of inhibitors to BRD2 and BRD4.

Molecular mechanism of inhibitor bindings to bromodomain-containing protein 9 explored based on molecular dynamics simulations and calculations of binding free energies

Abstract: Recently, bromodomain-containing protein 9 (BRD9) has been a prospective therapeutic target for anticancer drug design. Molecular dynamics (MD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were adopted to explore binding modes of three inhibitors (5SW, 5U2, and 5U6) to BRD9 and identify the hot spot of the inhibitor-BRD9 binding. The results indicate that the inhibitor 5SW has the strongest binding ability to BRD9 among the current three inhibitors. Furthermore, the rank of the binding free energies predicted by MM-GBSA approach agrees with that determined by the experimental values. In addition, inhibitor-residue interactions were computed by using residue-based free-energy decomposition method and the results suggest that residue His42 produces the CH-H interactions, residues Asn100, Ile53 and Val49 produce the CH-[Formula: see text] interactions with three inhibitors and Tyr106, Phe45 and Phe44 generate the π-π interactions with inhibitors. Notably, the residue Asn140 forms hydrogen bonding interactions with three inhibitors. This research is expected to provide useful molecular basis and dynamics information at atomic levels for the design of potent inhibitors inhibiting the activity of BRD9.

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

Abstract: The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.

Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis.

Abstract: It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together to uncover binding modes of inhibitors 8P9, 8PU, and 8PX to BRD4(1) The results obtained from binding free energy calculations show that van der Waals interactions act as the main regulator in bindings of inhibitors to BRD4(1) The information stemming from PCA reveals that inhibitor associations extremely affect conformational changes, internal dynamics, and movement patterns of BRD4(1) Residue-based free energy decomposition method was wielded to unveil contributions of independent residues to inhibitor bindings and the data signify that hydrogen bonding interactions and hydrophobic interactions are decisive factors affecting bindings of inhibitors to BRD4(1) Meanwhile, eight residues Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asn140, and Ile146 are recognized as the common hot interaction spots of three inhibitors with BRD4(1) The results from this work are expected to provide a meaningfully theoretical guidance for design and development of effective inhibitors inhibiting of the activity of BRD4

Application of Interspecies Correlation Estimation (ICE) models and QSAR in estimating species sensitivity to pesticides

Abstract: Ecological risk assessment is challenged by the need to assess hazard to the diverse communities of organisms inhabiting aquatic and terrestrial systems. Computational approaches, such as Quantitative Structure Activity Relationships (QSAR) and Interspecies Correlation Estimation (ICE) models, are useful tools that provide estimates of acute toxicity where data are lacking or limited for ecological risk assessments (ERA). This review describes the technical basis of ICE models for use in pesticide ERA that may be used in conjunction with QSAR model estimates or surrogate species toxicity data and demonstrates the potential for improving hazard assessment. Validation and uncertainty analysis of ICE model predictions are summarized and used as guidance for selecting ICE models and evaluating toxicity predictions. A user-friendly web-based ICE modelling platform (Web-ICE) is described and demonstrated through case studies. Case studies include the development of Species Sensitivity Distributions generated from QSAR and ICE estimates, comparative sensitivity for a pesticide and its degradate, and application of ICE-estimated toxicity values for listed species assessments.

Modelling of ready biodegradability based on combined public and industrial data sources

Abstract: The European Registration, Evaluation, Authorization and Restriction of Chemical Substances Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB), considering in s...

Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression.

Abstract: One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapt...

Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective

Abstract: The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds.

Classification models for predicting the antimalarial activity against Plasmodium falciparum

Abstract: Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 ...

Molecular docking-based classification and systematic QSAR analysis of indoles as Pim kinase inhibitors

Abstract: Pim kinase enzyme has an essential role in the treatment of prostate, colon and acute myeloid leukaemia cancers. The indoles inhibitors were docked in the enzyme’s active pocket in order to survey ...

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis.

Abstract: The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Cur...

A robust model for estimating thermal conductivity of liquid alkyl halides

Abstract: Thermal conductivity is an essential thermodynamic property in chemical engineering application. As a result, estimating the thermal conductivity of organic compounds is of significance in industry...

Modelling peak exposure of pesticides in terrestrial and aquatic ecosystems: importance of dissolved organic carbon and vertical particle movement in soil

Abstract: In the present work, an existing vegetation/air/litter/soil model (SoilPlusVeg) was modified to improve organic chemical fate description in terrestrial/aquatic ecosystems accounting for ho...

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach

Abstract: Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according...

Two spectral QSPR models of porphyrin macromolecules for chelating heavy metals and different ligands released from industrial solvents: CH2Cl2, CHCl3 and toluene

Abstract: Two simple and reliable correlations are introduced for the prediction of emission and absorption of porphyrins and their derivatives, i.e. metalloporphyrins and ligand coordinated metalloporphyrin...