Showing papers in "Sar and Qsar in Environmental Research in 2018"
TL;DR: This simple QSPR model can be used for the prediction of the adsorption coefficient of numerous aromatic compounds on MWCNTs and has shown robust, very simple, predictable and reliable models.
Abstract: In this investigation, quantitative structure–property relationship (QSPR) modelling of adsorption coefficients of 69 aromatic compounds on multi-wall carbon nanotubes (MWCNTs) was studied using th...
39 citations
TL;DR: Setrobuvir, YAK and, to a lesser extent, IDX-184 reveal promising results compared to other inhibitors in terms of binding ZIKV RdRp, which would be powerful anti-ZIKV drugs.
Abstract: A new Zika virus (ZIKV) outbreak started in 2015. According to the World Health Organization, 84 countries confirmed ZIKV infection. RNA-dependent RNA polymerase (RdRp) was an appealing target for ...
33 citations
TL;DR: Although PCR is the best validated and balanced technique, SVM always outperformed the other methods, when experimental values were the benchmark.
Abstract: Prediction performance often depends on the cross- and test validation protocols applied Several combinations of different cross-validation variants and model-building techniques were used
32 citations
TL;DR: A two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine and the experimental results show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.
Abstract: Quantitative structure-activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors
24 citations
TL;DR: It is demonstrated that the coumarin scaffold serves as a promising pharmacophore for MTDLs design and was found to have inhibitory properties at two key enzymes in disease relevant systems.
Abstract: Alzheimer's Disease (AD) is a neurodegenerative disease characterized by central nervous system insults with progressive cognitive (memory, attention) and non-cognitive (anxiety, depression) impairments. Pathophysiological events affect predominantly cholinergic neuronal loss and dysfunctions of the dopaminergic system. The aim of the current study was to design multi-targeted directed lead structures based on the coumarin scaffold with inhibitory properties at two key enzymes in disease relevant systems, i.e. acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Conventional and microwave synthetic methods were utilized to synthesize coumarin scaffold-based novel morpholino, piperidino, thiophene and erucic acid conjugates. Biological assays indicated that the coumarin-morpholine ether conjugate BPR 10 was the most potent hMAO-B inhibitor. The coumarin-piperidine conjugates BPR 13 and BPR 12 were the most potent inhibitors of eeAChE at 100 μM and 1 μM, respectively. Molecular modelling studies were conducted with Accelrys® Discovery Studio® V3.1.1 utilising the published hMAO-B (2V61) and hAChE (4EY7) crystal structures. Compound BPR 10 occupies both the entrance and substrate cavities of the active site of MAO-B. BPR 13 resides in both the peripheral anionic site (PAS) and the catalytic anionic site (CAS) of hAChE. This study demonstrated that the coumarin scaffold serves as a promising pharmacophore for MTDLs design.
24 citations
TL;DR: This ligand-based study serves as a crucial benchmark for further development of the HIV protease inhibitors with improved activities and also identifies the effective structural determinants for higher affinity against HIV PR.
Abstract: Multiple Quantitative Structure-Activity Relationship (QSAR) analysis is widely used in drug discovery for lead identification. Human Immunodeficiency Virus (HIV) protease is one of the key targets for the treatment of Acquired Immunodeficiency Syndrome (AIDS). One of the major challenges for the design of HIV-1 protease inhibitors (HIV PRIs) is to increase the inhibitory activities against the enzyme to a level where the problem associated to drug resistance may be considerably delayed. Herein, chemometric analyses were performed with 346 structurally diverse HIV PRIs with experimental bioactivities against a sub-type B mutant to develop highly predictable QSAR models and also to identify the effective structural determinants for higher affinity against HIV PR. The QSAR models were developed using OCHEM-based machine learning tools (ASNN, FSMLR, KNN, RF, MANN and XGBoost), with descriptors calculated by eight different software packages. Simultaneously, a Monte Carlo optimization-based QSAR modelling was performed using SMILES and graph-based descriptors to understand fragment and topochemical contributions. To validate the actual predictability of all these models, an additional set of 104 compounds (also with known experimental activities) with slightly different chemical space were employed. This ligand-based study serves as a crucial benchmark for further development of the HIV protease inhibitors with improved activities.
24 citations
TL;DR: Different drugs, showing various structures, have been repurposed to be used against ZIKV infection and the conditions for their potential use in practice are discussed.
Abstract: Zika virus (ZIKV) is a mosquito-borne flavivirus for which there are no vaccines or specific therapeutics. To find drugs active on the virus is a complex, expensive and time-consuming process. The prospect of drug repurposing, which consists of finding new indications for existing drugs, is an interesting alternative to expedite drug development for specific diseases. In theory, drug repurposing is also able to respond much more rapidly to a crisis than a classical drug discovery process. Consequently, the methodology is attractive for vector-borne diseases that can emerge or re-emerge worldwide with the risk to become pandemic quickly. Different drugs, showing various structures, have been repurposed to be used against ZIKV infection. They are reviewed in this study and the conditions for their potential use in practice are discussed.
22 citations
TL;DR: This study evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds, and used the new data to build a family of integrated models.
Abstract: Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.
19 citations
TL;DR: Docking analysis targeted to the whole enzyme revealed that the compounds exhibiting IC50 values higher than expected could bind to other peripheral sites with lower free energy, Eo, than when bound to the active/allosteric site.
Abstract: PTP1b is a protein tyrosine phosphatase involved in the inactivation of insulin receptor. Since inhibition of PTP1b may prolong the action of the receptor, PTP1b has become a drug target for the treatment of type II diabetes. In the present study, prediction of inhibition using docking analysis targeted specifically to the active or allosteric site was performed on 87 compounds structurally belonging to 10 different groups. Two groups, consisting of 15 thiomorpholine and 10 thiazolyl derivatives exhibiting the best prediction results, were selected for in vitro evaluation. All thiomorpholines showed inhibitory action (with IC50 = 4-45 μΜ, Ki = 2-23 μM), while only three thiazolyl derivatives showed low inhibition (best IC50 = 18 μΜ, Ki = 9 μΜ). However, free binding energy (E) was in accordance with the IC50 values only for some compounds. Docking analysis targeted to the whole enzyme revealed that the compounds exhibiting IC50 values higher than expected could bind to other peripheral sites with lower free energy, Eo, than when bound to the active/allosteric site. A prediction factor, E- (ΣEo × 0.16), which takes into account lower energy binding to peripheral sites, was proposed and was found to correlate well with the IC50 values following an asymmetrical sigmoidal equation with r2 = 0.9692.
19 citations
TL;DR: Comparison of the novel adamantanyl derivatives with the 2-thiazolylimino-5-arylidene-4- thiazolidinones previously tested showed that insertion of the adamantanyl group led to the production of more potent COX-1 inhibitors, as well as LOX inhibitors (increased activity from 200% to 560%).
Abstract: Docking analysis was used to predict the effectiveness of adamantanyl insertion in improving cycloxygenase/lipoxygenase (COX/LOX) inhibitory action of previously tested 2-thiazolylimino-5-arylidene-4-thiazolidinones. The crystal structure data of human 5-LOX (3O8Y), ovine COX-1 (1EQH) and mouse COX-2 (3ln1) were used for docking analysis. All docking calculations were carried out using AutoDock 4.2 software. Following prediction results, 11 adamantanyl derivatives were synthesized and evaluated for biological action. Prediction evaluations correlated well with experimental biological results. Comparison of the novel adamantanyl derivatives with the 2-thiazolylimino-5-arylidene-4-thiazolidinones previously tested showed that insertion of the adamantanyl group led to the production of more potent COX-1 inhibitors, as well as LOX inhibitors (increased activity from 200% to 560%). Five compounds out of the 11 exhibited better activity than naproxen; while nine out of 11 showed better activity than NDGA and seven compounds possessed better anti-inflammatory activity than indomethacin.
17 citations
TL;DR: The combinatorial design of a series of 3850 fluoroquinolone and isothiazoloquinolones compounds was designed and screened virtually by applying a topological descriptor based quantitative structure activity relationship (QSAR) for predicting highly active congeneric quinolone leads against Mycobacterium fortuitum and Myc Cobacterium smegmatis.
Abstract: The virulence of tuberculosis infections resistant to conventional combination drug regimens cries for the design of potent fluoroquinolone compounds to be used as second line antimycobacterial chemotherapeutics. One of the most effective in silico methods is combinatorial design and high throughput screening by a ligand-based pharmacophore prior to experiment. The combinatorial design of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds was then screened virtually by applying a topological descriptor based quantitative structure activity relationship (QSAR) for predicting highly active congeneric quinolone leads against Mycobacterium fortuitum and Mycobacterium smegmatis. The predicted highly active congeneric hits were then subjected to a comparative study between existing lead sparfloxacin with fluoroquinolone FQ hits as well as ACH-702 with predicted active isothiazoloquinolones, utilizing pharmacophore modelling to focus on the mechanism of drug binding against mycobacterial DNA gyrase. Finally, 68 compounds including 34 FQ and 34 isothiazoloquinolones were screened through high throughput screening comprising QSAR, the Lipinski rule of five and ligand-based pharmacophore modelling.
TL;DR: It was found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions, however, balanced accuracies for the two datasets were found to be 79% and 82%.
Abstract: Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.
TL;DR: A. aspera has shown a significant antidepressant effect, possibly due to hexatriacontane, by raising monoamine levels by raising serotonin levels.
Abstract: Traditional knowledge guides the use of plants for restricted therapeutic indications, but their pharmacological actions may be found beyond their ethnic therapeutic indications employing emerging computational tools. In this context, the present study was envisaged to explore the novel pharmacological effect of Achyranthes aspera (A. aspera) using PASS and PharmaExpert software tools. Based on the predicted mechanisms of the antidepressant effect for all analysed phytoconstituents of A. aspera, one may suggest its significant antidepressant action. The possible mechanism of this novel pharmacological effect is the enhancement of serotonin release, in particular caused by hexatriacontane. Therefore, pharmacological validation of the methanolic extract, hexatriacontane rich (HRF) and hexatriacontane lacking fraction (HLF) of A. aspera was carried out using the Forced Swimming Test and Tail suspension test in mice. The cortical and hippocampal monoamine and their metabolite levels were measured using high performance liquid chromatography (HPLC). A. aspera methanolic extract, HRF treatments showed a significant antidepressant effect comparable to imipramine. Further, the corresponding surge in cortical and hippocampal monoamine and their metabolite levels was also observed with these treatments. In conclusion, A. aspera has shown a significant antidepressant effect, possibly due to hexatriacontane, by raising monoamine levels.
TL;DR: Important and crucial structural features have been identified that may be responsible for enhancing the activity profile of these hydroxylamine compounds and may be utilized further in designing promising HIV-1 protease inhibitors of this class.
Abstract: The current study deals with chemometric modelling strategies (Naive Bayes classification, hologram-based quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)) to explore the important features of hydroxylamine derivatives for exerting potent human immunodeficiency virus-1 (HIV-1) protease inhibition. Depending on the statistically validated reliable and robust quantitative structure-activity relationship (QSAR) models, important and crucial structural features have been identified that may be responsible for enhancing the activity profile of these hydroxylamine compounds. Arylsulfonamide function along with methoxy or fluoro substitution is important for enhancing activity. Bulky steric substitution at the sulfonamide nitrogen disfavours activity whereas smaller hydrophobic substitution at the same position is found to be favourable. Apart from the crucial oxazolidinone moiety, pyrrolidine, cyclic urea and methyl ester functions are also responsible for increasing the HIV-1 protease inhibitory profile. Observations derived from these modelling studies may be utilized further in designing promising HIV-1 protease inhibitors of this class.
TL;DR: Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies and newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.
Abstract: Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.
TL;DR: This study built quantitative structure–property relationship models for the prediction of the glass transition temperatures of polymers using a data set of 206 diverse polymers and used a true external set to demonstrate the performance of the developed models.
Abstract: The glass transition temperature is a vital property of polymers with a direct impact on their stability. In the present study, we built quantitative structure-property relationship models for the prediction of the glass transition temperatures of polymers using a data set of 206 diverse polymers. Various 2D molecular descriptors were computed from the single repeating units of polymers. We derived five models from different combinations of six descriptors in each case by employing the double cross-validation technique followed by partial least squares regression. The selected models were subsequently validated by methods such as cross-validation, external validation using test set compounds, the Y-randomization (Y-scrambling) test and an applicability domain study of the developed models. All of the models have statistically significant metric values such as r2 ranging from 0.713-0.759, Q2 ranging from 0.662-0.724 and [Formula: see text] ranging 0.702-0.805. Finally, a comparison was made with recently published models, though the previous models were based on a much smaller data set with limited diversity. We also used a true external set to demonstrate the performance of our developed models, which may be used for the prediction and design of novel polymers prior to their synthesis.
TL;DR: A critical analysis of the SAR and QSAR models was made focusing on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models.
Abstract: Repellents disrupt the behaviour of blood-seeking mosquitoes protecting humans against their bites which can transmit serious diseases. Since the mid-1950s, N,N-diethyl-m-toluamide (DEET) is considered as the standard mosquito repellent worldwide. However, DEET presents numerous shortcomings. Faced with the heightening risk of mosquito expansion caused by global climate changes and increasing international exchanges, there is an urgent need for a better repellent than DEET and the very few other commercialised repelling molecules such as picaridin and IR3535. In silico approaches have been used to find new repellents and to provide better insights into their mechanism of action. In this context, the goal of our study was to retrieve from the literature all the papers dealing with qualitative and quantitative structure-activity relationships on mosquito repellents. A critical analysis of the SAR and QSAR models was made focusing on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed. The hypotheses to compute homology and pharmacophore models, their interest to find new repellents and to provide insights into the mechanisms of repellency in mosquitoes were analysed. The interest to consider the mosquito olfactory system as the target to compute new repellents was discussed. The potential environmental impact of these chemicals as well as new ways of research were addressed.
TL;DR: A benchmark dataset of very diverse drugs is used for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms, generating robust regression models in the training and external validation stages.
Abstract: The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.
TL;DR: This work attempts to use a three-dimensional interaction profile of the active site of a class of proteins, identify selective positions for the binding of functional groups, called features, and develop ensembles of multi-targeted pharmacophores that retain specificity and selectivity.
Abstract: Health care systems have benefitted from rational drug discovery processes like vHTS, virtual high throughput screening pharmacophores and quantitative structure-activity relationships, and many challenges have been explored using such techniques: decisions on specificity and selectivity are made after screening millions of molecules for multiple targets. Recent challenges in drug research emphasize the design of drugs that bind with more than one target of interest (multi-target) and do not bind with undesirable targets. This work attempts to use a three-dimensional interaction profile of the active site of a class of proteins, identify selective positions for the binding of functional groups, called features, and develop ensembles of multi-targeted pharmacophores that retain specificity and selectivity. The goal of this study is to develop multi-target pharmacophores by computational methods using protein structures alone to guide the discovery of novel inhibitors of plasmepsins, displaying selectivity over their human homologs, cathepsin D and pepsin. The development of such novel tools is attempted using a combination of different approaches such as the molecular interaction field, clique graph and inductive logic programming to identify and compare specific and selective complementary features. The identification of selective combinations of features has resulted in the design of multi-featured specific and selective pharmacophores that are validated using antimalarial compounds in ChEMBL that are known for their anti-plasmepsin II activity. This novel method is computationally less intensive and is applicable to any known class of target structures for finding specific and selective binders simultaneously.
TL;DR: A ‘pseudo-consensus’ 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an ‘average’ pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues.
Abstract: The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H37Rv strain We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern Consequently, a 'pseudo-consensus' 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an 'average' pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles
TL;DR: This work developed and validated counter-propagation artificial neural network (CPANN) models for in silico evaluation of toxicity of pesticides towards honeybees by using new in-house software and gives reliable predictions in an external validation set and cover a large structural space.
Abstract: Nowadays, environmental and biological endpoints can be predicted with in silico approaches if sufficient experimental data of good quality are available Since the experimental evaluation of acute contact toxicity towards honeybees (Apis mellifera) is a complex and expensive assay, the computational models that follow OECD principles for this endpoint prediction represent important alternatives for safety prioritisation of chemicals, especially pesticides We developed and validated counter-propagation artificial neural network (CPANN) models for in silico evaluation of toxicity of pesticides towards honeybees by using new in-house software The data set included 254 pesticides with their toxicological experimental values (acute contact toxicity after 48 h of exposure - LD50 [μg/bee]) The 2D structures of compounds were mathematically represented with 56 Dragon molecular descriptors (MDs) The two-category models were developed to separate compounds as toxic or non-toxic for two different thresholds: (i) toxic when LD50 < 1 μg/bee and (ii) toxic when LD50 < 100 μg/bee The models give reliable predictions in an external validation set and cover a large structural space They were applied to a structurally diverse data set of 395 experimentally untested pesticides; 19% of them were predicted as highly toxic towards bees
TL;DR: This study has modelled the experimentally derived PBT index data using an extended topological atom (ETA) along with constitutional descriptors to show the usefulness of the ETA indices in modelling the endpoint, and developed models that can be used in PBT hazard screening for identification and prioritization of chemicals from the structural information alone.
Abstract: Persistent, bioaccumulative and toxic (PBT) chemicals symbolize a group of substances that are not easily degraded; instead, they accumulate in different organisms and exhibit an acute or c...
TL;DR: Based on the evaluation results, GastroPlus™ can be used as a QSAR/PBPK tool for toxicokinetic parameter predictions and was consistent with those Css values calculated by in vitro-to-in vivo extrapolation (IVIVE) approaches using experimental parameters.
Abstract: The accurate prediction of toxicokinetic parameters arising from oral, dermal and inhalation routes of chemical exposure is a key element in chemical safety assessments. In this research, the physi...
TL;DR: The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition and the computer-aided design of new compounds, as potential ACE inhibitors, is presented.
Abstract: Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative...
TL;DR: Weaknesses and strengths of some prominent (Q)SAR programs and diverse combinatorial options in the prediction of skin sensitization by pesticidal AS are shown to foster discussions on in silico alternatives to animal testing in the pesticide area.
Abstract: In vivo skin sensitization assays have to be provided by applicants to the competent authorities in the European Union for the approval of active substances (AS) in pesticides. This study aimed to ...
TL;DR: Findings indicate potential START domains and their binding levels with toxic chemicals; sorted viewpoints could be useful as a promising way to identify chemicals with related steroidogenisis impacts on human health.
Abstract: In this study, cholesterol biotransformation gene-set of human steroidogenic acute regulatory protein-related lipid transfer (START) domains were evaluated from high-throughput gene screening approaches. It was shown that STARD1, STARD3 and STARD4 proteins are better effective transporters of cholesterol than STARD5 and STARD6 domains. Docking studies show a strong agreement with gene ontology enrichment data. According to both complementary strategies, it was found that only STARD1, STARD3 and STARD4 are potentially involved in cholesterol biotransformation in mitochondria through Ω1-loop of C-terminal α4-helical domain. Ensemble docking assessment for a set of selected chemicals of protein-chemical networks has shown possible binding probabilities with START domains. Among those, reproductive toxicity evoked drugs (mifepristone), insecticides (rotenone), tobacco pulmonary carcinogens (benzo(a)pyrene) and endocrine disruptor chemicals (EDCs) including perfluorooctanesulfonic acid (PFOS) and aflatoxin B1 (AFB1) potentially bound with novel hotspot residues of the α4-helical domain. Compound representation space and clustering approaches reveal that the START proteins show more sensitivity with these lead scaffolds, so they could provide probable barrier assets in cholesterol and steroidogenic acute regulatory (StAR) binding and leads adverse consequences in steroidogenesis. These findings indicate potential START domains and their binding levels with toxic chemicals; sorted viewpoints could be useful as a promising way to identify chemicals with related steroidogenisis impacts on human health.
TL;DR: In silico screening of a small-molecule chemical library against the acetyl–lysine binding site of the first bromodomain (BD1) in BRD4 protein, identified potential inhibitors form direct and water-mediated hydrogen bonds with higher occupancy which may contribute to ligand specificity towards BRD 4-BD1.
Abstract: Bromodomain-containing protein 4 (BRD4) is a member of the bromodomain and extra-terminal domain (BET) family of proteins. It epigentically regulates the transcription of growth-promoting genes and has become an attractive target for the development of anticancer and anti-inflammatory agents. In the current study, we performed an in silico screening of a small-molecule chemical library against the acetyl-lysine binding site of the first bromodomain (BD1) in BRD4 protein. Potential inhibitors identified through virtual screening were further studied through molecular dynamics simulations, water entrapment analysis and Molecular Mechanics (MM)/Poisson-Boltzmann surface area (PBSA) binding free energy calculations. Many of the identified compounds exhibit better G-score (-11.64 kcal∙mol-1 to -10.31 kcal∙mol-1) and predicted binding affinity (-9.66 kcal∙mol-1 to -6.63 kcal∙mol-1) values towards BRD4-BD1 than that of the reference compound (+)-JQ1. Molecular dynamics simulation studies show that in free-form BRD4 the reported conserved water molecules are not retained at their specific positoins due to flexibiliy in the ZA-loop. In BRD4-ligand complexes the number and positions of conserved water molecules depends on the bound ligand. Identified potential inhibitors bind stably at the acetyl-lysine binding pocket of BRD4 and form direct and water-mediated hydrogen bonds with higher occupancy which may contribute to ligand specificity towards BRD4-BD1. Further, through MM/PBSA we calculated the binding free energies of selected compounds, which shows that they have comparable energies to that of (+)-JQ1, while NSC744713 shows better binding free energy.
TL;DR: The QSTR models generated for AhR binding affinities of chemicals with TCDD/TCDF-like effects were internally and externally validated in line with the Organization of Economic Co–operation and Development (OECD) principles.
Abstract: Toxic potencies of xenobiotics such as halogenated aromatic hydrocarbons inducing 2,3,7,8-tetrachlorodibenzo-p-dioxin/2,3,7,8-tetrachlorodibenzofuran (TCDD/TCDF)-like effects were investigated by quantitative structure-toxicity relationships (QSTR) using their aryl hydrocarbon receptor (AhR) binding affinity data. A descriptor pool was created using the SPARTAN 10, DRAGON 6.0 and ADMET 8.0 software packages, and the descriptors were selected using QSARINS (v.2.2.1) software. The QSTR models generated for AhR binding affinities of chemicals with TCDD/TCDF-like effects were internally and externally validated in line with the Organization of Economic Co-operation and Development (OECD) principles. The TCDD-based model had six descriptors from DRAGON 6.0 and ADMET 8.0, whereas the TCDF-based model had seven descriptors from DRAGON 6.0. The predictive ability of the generated models was tested on a diverse group of chemicals including polychlorinated/brominated biphenyls, dioxins/furans, ethers, polyaromatic hydrocarbons with fused heterocyclic rings (i.e. phenoxathiins, thianthrenes and dibenzothiophenes) and polyaromatic hydrocarbons (i.e. halogenated naphthalenes and phenanthrenes) with no AhR binding data. For the external set chemicals, the structural coverage of the generated models was 90% and 89% for TCDD and TCDF-like effects, respectively.
TL;DR: It is shown that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
Abstract: This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
TL;DR: The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds.
Abstract: The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.