2D Autocorrelation Descriptors Research Articles

In Silico Exploration of Molecular Mechanisms for Inhibiting Inflammatory Responses by 3Н-Thiazolo[4,5-b]pyridin-2-one Derivatives.

Combined in silico strategy for molecular mechanisms exploration of a series 3H-thiazolo[4,5-b]pyridin-2-ones exhibiting strong anti-exudative action through QSAR analysis, molecular docking and pharmacophore modelling is reported. GA-ML technique was used for QSAR models generation with 2D autocorrelation descriptors. One- and two-parameter regressions revealed that certain structural patterns or heteroatoms contribute mutually to the anti-exudative activity potentiation. Possible action mechanisms were discovered through flexible docking simulations with cyclooxygenase pathway enzymes (COX-1, COX-2, mPGES-1). Docking results indicated the possibility of stable complexes formation with the effective docking scores and proper orientation of ligands within the enzymes active sites. Pharmacophore modelling was carried out using protein-ligand interaction fingerprints methodology. Two- and three-centre 3D pharmacophore queries were constructed. Their analysis indicated the functionality of bicyclic thiazolopyridine scaffold proved by the steric placement of heteroatoms in the corresponding pharmacophore centres.

Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure–activity relationship models

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

Computational study on subfamilies of piperidine derivatives: QSAR modelling, model external verification, the inter-subset similarity determination, and structure-based drug designing

ABSTRACT A new subset of furan-pyrazole piperidine derivatives was used for QSAR model development. These compounds exhibit good Akt1 inhibitory activity; moreover, antiproliferative activities in vitro against OVCAR-8 (Human ovarian carcinoma cells) and HCT116 (human colon cancer cells), were confirmed for them. Based on the relevant three-dimensional (3D) and 2D autocorrelation descriptors, selected by genetic algorithm (GA), multiple linear regression (MLR) was established on half maximal-inhibitory concentration (IC50), in Akt1 and cancer cell lines independently. Robustness, stability, and predictive ability of the models were evaluated using external and internal validation (r 2: 0.742–0.832, Q 2 LOO: 0.684–0.796, RMSE: 0.247–0.299, F: 32.283–57.578, and r 2 y-random: 0.049–0.080). Furthermore, in the new strategy, each of the evaluated models was generalized to two other subfamilies of piperidines to simultaneously compare the activities and structural similarity of these three subsets. Probably, structural similarity can be more considered as a criterion of similarity in the mechanism of action. Also, external verification of suggested predictive models was performed by another subset. Finally, by focusing on M64 as the most potent in vivo antitumor compound, 15 new derivatives were designed and six potent candidates were proposed for further investigation.

Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 Antagonists.

Several years ago, the crystallographic structures of the transient receptor potential vanilloid 1 (TRPV1) in the presence of agonists and antagonists were reported, providing structural information about its chemical activation and inactivation. TRPV1’s activation increases the transport of calcium and sodium ions, leading to the excitation of sensory neurons and the perception of pain. On the other hand, its antagonistic inactivation has been explored to design analgesic drugs. The interactions between the antagonists 5,5-diarylpentadienamides (DPDAs) and TRPV1 were studied here to explain why they inactivate TRPV1. The present work identified the structural features of TRPV1–DPDA complexes, starting with a consideration of the orientations of the ligands inside the TRPV1 binding site by using molecular docking. After this, a chemometrics analysis was performed (i) to compare the orientations of the antagonists (by using LigRMSD), (ii) to describe the recurrent interactions between the protein residues and ligand groups in the complexes (by using interaction fingerprints), and (iii) to describe the relationship between topological features of the ligands and their differential antagonistic activities (by using a quantitative structure–activity relationship (QSAR) with 2D autocorrelation descriptors). The interactions between the DPDA groups and the residues Y511, S512, T550, R557, and E570 (with a recognized role in the binding of classic ligands), and the occupancy of isoquinoline or 3-hydroxy-3,4-dihydroquinolin-2(1H)-one groups of the DPDAs in the vanilloid pocket of TRPV1 were clearly described. Based on the results, the structural features that explain why DPDAs inactivate TRPV1 were clearly exposed. These features can be considered for the design of novel TRPV1 antagonists.

Quinazoline analogues as cytotoxic agents; QSAR, docking, and in silico studies.

Background and purpose:Synthesis and investigation of pharmacological activity of novel compounds are time and money-consuming. However, computational techniques, docking, and in silico studies have facilitated drug discovery research to design pharmacologically effective compounds.Experimental approach:In this study, a series of quinazoline derivatives were applied to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometric methods were conducted to provide relations between structural features and cytotoxic activity of a variety of quinazoline derivatives against breast cancer cell line. An in silico-screening was accomplished and new impressive lead compounds were designed to target the epidermal growth factor receptor (EGFR)-active site based on a new structural pattern. Molecular docking was performed to delve into the interactions, free binding energy, and molecular binding mode of the compounds against the EGFR target.Findings/Results:A comparison between different methods significantly indicated that genetic algorithm-partial least-squares were selected as the best model for quinazoline derivatives. In the current study, constitutional, functional, chemical, resource description framework, 2D autocorrelation, and charge descriptors were considered as significant parameters for the prediction of anticancer activity of quinazoline derivatives. In silico screening was employed to discover new compounds with good potential as anticancer agents and suggested to be synthesized. Also, the binding energy of docking simulation showed desired correlation with QSAR and experimental data.Conclusion and implications:The results showed good accordance between binding energy and QSAR results. Compounds Q1-Q30 are desired to be synthesized and applied to in vitro evaluation.

Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells.

SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effect of diverse chemical compounds on this cancer cell line. The dataset of cytotoxic and inactive compounds were downloaded from the PubChem database. It contains the data for all chemical compounds for which cytotoxicity results expressed by GI50 was recorded. In total 13 blocks of molecular descriptors were computed and used, after appropriate pre-processing in building QSAR models with four machine learning classifiers: Random forest (RF), gradient boosting, support vector machine and random k-nearest neighbors. Among the 186 models reported none had a positive predictive value (PPV) higher than 0.90 in both nested cross-validation and on an external dataset testing, but 7 models had a PPV higher than 0.85 in both evaluations, all seven using the RFs algorithm as a classifier, and topological descriptors, information indices, 2D-autocorrelation descriptors, P-VSA-like descriptors, and edge-adjacency descriptors as sets of features used for classification. The y-scrambling test was associated with considerably worse performance (confirming the non-random character of the models) and the applicability domain was assessed through three different methods.

Role of 2-Dimensional Autocorrelation Descriptors in Predicting Antimalarial Activity of Artemisinin and its Aanalogues: A QSAR Study.

Malaria, one of the World's biggest billers' is on the schedule for biomedical research and public health policies. The introduction of the artemisinin, a Chinese traditional drug from Artemisia annua is a revolution in the treatment of malaria. Artemisinin-based combination treatment (ACT) is considered to be the best strategy for uncomplicated Falciparum malaria. The presence of 1,2,4-trioxane system in artemisinin is responsible for its antimalarial activity. In this study, twenty-nine analogues of artemisinin were taken into account for QSAR studies along with artemisinin. The most active analogue of artemisinin 21 was energy minimized. All the structures were prepared from the active conformer 21 and energy was minimized to the stable state using MMFF94 force field using ChemBioDraw-12. Genetic Algorithm is used to decide the descriptors best required for the model generation. The test set and training set division were done by using hierarchal clustering module available with NCSS statistical software. The antimalarial activity of the artemisinin and its substituted analogues has been analyzed through the multiple linear regression (MLR) using various physiochemical and structural descriptors obtained from PADEL software. The models were prepared using the Sigma Plot version 11. The calculated 2D autocorrelation descriptors and the MLR model suggest that artemisinin and its analogues hold the scope in the optimization of antimalarial activity.

Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity

Two approaches of building up predictive models for acetylcholinesterase inhibitory activity of a broad dataset of 403 compounds, 2D Autocorrelation descriptors and 2D Monte Carlo descriptors, were compared. In a first approach, the molecular information has been encoded in 2D autocorrelation descriptors, obtained from different weighting schemes. Quantitative structure-activity relationships were built by multiple linear regression combined with evolutionary search. All 2D autocorrelation models were predictive according to internal and external validation experiments, including more than 30 descriptors. The 2D Autocorrelation space brings a major influence of Sanderson electronegativity weighted terms for three different splits. The Monte Carlo descriptors were calculated with the CORAL software (http://www.insilico.eu/coral). Advantages and disadvantages of the above approaches are discussed.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Synthesis, antileishmanial activity and QSAR study of (1,3,4-thiadiazol-2-ylthio) acetamides derived from 5-nitrofuran

A novel series of (1,3,4-thiadiazol-2-ylthio)acetamides derived from 5-nitrofuran were synthesized and evaluated against extracellular promastigotes of Leishmania major. The most potent anti-promastigote compounds were also evaluated in vitro against intracellular amastigotes. All compounds showed better activity than standard drug Glucantime. The most potent compounds against the promastigotes were found to be N-(3,4-dimethoxyphenethyl)-2-(5-(5-nitrofuran-2-yl)-1,3,4-thiadiazol-2-ylthio) acetamide (5q) and 2-(5-(5-nitrofuran-2-yl)-1,3,4-thiadiazol-2-ylthio)-N-propylacetamide (5r) with IC50 values less than 20 µM. Although, the cytotoxic evaluation of target compounds against mouse peritoneal macrophages demonstrated that these series of compounds have cytotoxicity at concentrations higher than 50 µM, but most of them exhibited antileishmanial activity at non-cytotoxic concentrations. QSAR study indicated that 2D-autocorrelation and topological descriptors are influential parameters in the antileishmanial activity.

CP-MLR/PLS-directed QSAR studies on the antimalarial activity and cytotoxicity of substituted 4-aminoquinolines

A QSAR study has been carried out on antimalarial activity and cytotoxicity of substituted 4-aminoquinolines using topological and structure descriptors from DRAGON software. The descriptors, relevant to each activity, were identified by Combinatorial Protocol in Multiple Linear Regression (CP-MLR) approach. The different descriptors were identified for the antimalarial activities and cytotoxicity. The antimalarial activities are correlated with Topological, 2D autocorrelation, and functional class descriptors. The cytotoxicity shows correlation with atom centered functional descriptors. The identified descriptors have been used for the development of QSAR models. The models suggested that analogs containing aromatic primary amines, aliphatic secondary amines, and devoid of aliphatic secondary amides would show better antimalarial activity. The presence of aromatic ethers, CH2R2, and CH3X molecular fragments contribute for cytotoxicity. The study gives a direction for the future exploration of the chemical space for 4-aminoquinolines to modulate antimalarial activity and cytotoxicity.

Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors

Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9% for SOM1, 92.6% for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.

In-silico prediction of blood–brain barrier permeability

The ability of penetration of the blood–brain barrier is one of the significant properties of a drug or drug-like compound for the central nervous system (CNS), which is commonly expressed by log BB (log BB = log (C brain/C blood)). In this work, a dataset of 320 compounds with log BB values was split into a training set including 198 compounds and a test set including 122 compounds according to their structure properties by a Kohonen's self-organizing map (SOM). Each molecule was represented by global and shape descriptors, 2D autocorrelation descriptors and RDF descriptors calculated by ADRIANA.Code. Several quantitative models for prediction of log BB were built by a multilinear regression (MLR), a support vector machine (SVM) and an artificial neural network (ANN) analysis. The models show good prediction performance on the test set compounds.

Chemometric Modeling of 5-Phenylthiophenecarboxylic Acid Derivatives as Anti-Rheumatic Agents

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.

Classification of HCV NS5B Polymerase Inhibitors Using Support Vector Machine

Using a support vector machine (SVM), three classification models were built to predict whether a compound is an active or weakly active inhibitor based on a dataset of 386 hepatitis C virus (HCV) NS5B polymerase NNIs (non-nucleoside analogue inhibitors) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors, 2D and 3D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Three models were developed with the combination of different types of descriptors. Model 2 based on 16 global and 2D autocorrelation descriptors gave the highest prediction accuracy of 88.24% and MCC (Matthews correlation coefficient) of 0.789 on test set. Model 1 based on 13 global descriptors showed the highest prediction accuracy of 86.25% and MCC of 0.732 on external test set (including 80 compounds). Some molecular properties such as molecular shape descriptors (InertiaZ, InertiaX and Span), number of rotatable bonds (NRotBond), water solubility (LogS), and hydrogen bonding related descriptors performed important roles in the interactions between the ligand and NS5B polymerase.

CP-MLR/PLS directed QSAR study on the glutaminyl cyclase inhibitory activity of imidazoles: rationales to advance the understanding of activity profile

The glutaminyl cyclase inhibitory activity of a series of imidazoles has been analyzed through combinatorial protocol in multiple linear regressions (CP-MLR) and partial least square using different topological and structural descriptors. The QC activity was found to be correlated with 2D-autocorrelation (2DAUTO) and atom centered fragments (ACF) descriptors. The descriptor from 2DAUTO class showed that molecular structure frames of one, six and seven path length associated with atomic van der Waals volumes and polarizability hold scope for modulating QC inhibitory activity. The ACF descriptors suggested that the unsubstituted alkyl fragments and methyl substituted imidazole ring are favorable, while unsaturation in the same and C=N-C≡N are unfavorable for activity. The molar refractivity (MR) is conducive for activity. The descriptors identified in the study collectively highlight the significance of molecular volume and polarizability to the QC inhibitory activity of imidazoles. The models are statistically significant and showed good predictivity.

QSAR Study of 4‐Aryl‐4H‐Chromenes as a New Series of Apoptosis Inducers Using Different Chemometric Tools

The apoptosis-inducing activity data of a series of 4-aryl-4H-chromenes based on three cell lines (human breast cancer cell line T47D, human non-smal cell lung cancer cell line H1299, and human colorectal cancer cell line DLD-1) have been subjected to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometrics methods including multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were employed to make connections between structural parameters and induction of apoptosis in three different cell lines. Models of high statistical qualities were obtained for each cell line using GA-PLS method. The results revealed that 2D autocorrelation descriptors and dipole moments as a quantum chemical parameter are important structural parameters that significantly influence the activity in all three types of cell lines. However, the determinant descriptors for activity of compounds in H1299 cell line were partly different from the two other cell lines, which might be deduce that the studied compounds induce apoptosis through a different mechanism of action.

Quantitative Structure-Activity Relationship Analysis of the Anticonvulsant Activity of Some Benzylacetamides Based on Genetic Algorithm-Based Multiple Linear Regression

Purpose: To develop the quantitative structure-activity relationship (QSAR) for predicting the anticonvulsant activity of α-substituted acetamido-N-benzylacetamide derivatives.Methods: AM1 semiempirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. Two types of molecular descriptors, including the 2D autocorrelation and GETAWAY descriptors, were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach.Results: The high value of the correlation coefficient, R2 (0.900), indicate that the model was satisfactory.Conclusion: The proposed model has good stability, robustness and predictability when verified by internal and external validation.Keywords: Anticonvulsant, Benzylacetamides, 2D Autocorrelation, Quantitative structure-activity relationships, Multiple linear regression.

Docking and quantitative structure–activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC(50) values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.

2D autocorrelation modelling of the anti-HIV HEPT analogues using multiple linear regression approaches

A quantitative structure–anti HIV-1 activity relationship study has been applied in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine] analogues acting as non-nucleoside reverse transcriptase inhibitors. The relevant 2D autocorrelation descriptors for deriving a quantitative relation between the anti-HIV activity and structural properties were selected by the multiple linear regression approach. Analysis of the resulting model revealed a correlation coefficient and a root mean square error of 0.859 and 0.503, respectively. The predictive ability of the model indicates that this model can be used for virtual library screening of databases for novel potent anti-HIV agents.