Alignment-independent Descriptors Research Articles

2D-QSAR Study of Fluoroquinolone Derivatives:An Approach to Design Anti-tubercular Agents

QSAR studies were performed on a set of 39 analogs of fluoroquinolone using MDS vlife science QSAR plus module by using. Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares (PLS) Regression methods. Among these, MLR method has shown a very promising result as compared to other two methods and a QSAR model was generated by a training set of 28 molecules with correlation coefficient (r2) of 0.9412, significant cross validated correlation coefficient (q2) of 0.9213 and F test of 38.8480. In the selected descriptors, estate contribution, chi, path cluster and alignment independent descriptors were the most important descriptors in predicting anti-tubercular inhibitory activity.

3D-QSAR study of adenosine 5’-phosphosulfate (APS) analouges as ligands for APS reductase

Metabolism of sulfur (sulfur assimilation pathway, SAP) is one of the key pathways for the pathogenesis and survival of persistant bacteria, such as Mycobacterium tuberculosis (Mtb), in the latent period. Adenosine 5'-phosphosulfate reductase (APSR) is an important enzyme involved in the SAP, absent from the human body, so it might represent a valid target for development of new antituberculosis drugs. This work aimed to develop 3D-QSAR model based on the crystal structure of APSR from Pseudomonas aeruginosa, which shows high degree of homology with APSR from Mtb, in complex with its substrate, adenosine 5'-phosphosulfate (APS). 3D-QSAR model was built from a set of 16 nucleotide analogues of APS using alignment-independent descriptors derived from molecular interaction fields (MIF). The model improves the understanding of the key characteristics of molecules necessary for the interaction with target, and enables the rational design of novel small molecule inhibitors of Mtb APSR.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon.

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (IKr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K+ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R2 of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

QSAR and molecular docking studies on 4-quinoline carboxylic acid derivatives as inhibition of vesicular stomatitis virus replication

The current study describes the development of in silico models based on quantitative structure-activity relationship (QSAR) analysis has been performed on 4-quinoline carboxylic acid derivatives as inhibition capacity of vesicular stomatitis virus replication in Madin Darby canine kidney epithelial cells. A highly descriptive and predictive QSAR model was obtained through the calculation of alignment-independent descriptors using MOE 2009.10 software. For a training set of 20 compounds, the partial least squares analyses result in a model which displays a squared correlation coefficient (r2) of 0.913. Validation of this model was performed using leave-one-out (q2) of 0.842. This model gives (r2pre) of 0.889 for a test set of five compounds. Docking studies were performed for 25 compounds to investigate the mode of interaction between 4-quinoline carboxylic acid derivatives and the active site of the human dihydroorotate dehydrogenase.

Rational design and synthesis of new tetralin-sulfonamide derivatives as potent anti-diabetics and DPP-4 inhibitors: 2D & 3D QSAR, in vivo radiolabeling and bio distribution studies

Type 2 diabetes (T2D) is a severe disease and it is one of the most raising problems worldwide. This study deals with design, synthesis and in vivo determination of a new set of tetralin-sulfonamide derivatives as anti-diabetic and dipeptidyl peptidase-IV (DPP-4) inhibiting agents. Most of the new compounds exhibited significant hypoglycemic effect alongside with DPP-4 suppression potency considering sitagliptin as a reference drug. The most promising compounds 4, 15 showed 2.80 nM DPP-4 IC50 with 20–40 folds selectivity over DPP-8 and DPP-9. 2D and 3D QSAR models were performed using auto QSAR of Schrödinger, QuaSAR of MOE and 3D Field-based QSAR of Schrödinger, respectively. The experimental results revealed that the alignment-independent descriptors, electrostatic and steric field descriptors were significantly correlated with the antidiabetic activity of the new derivatives. In addition, the new compounds were docked in the active site of DPP-4 in reference to sitagliptin to rationalize the binding modes of the compounds with the amino acid residues of the enzyme. Furthermore, 131I-compound 4 complex was selected to evaluate the pharmacokinetic behavioral profile of compound 4 and its body organs uptakes alongside its elimination pathway as a representative example for the rest of the analogues. The bio distribution pattern of the tracer proved the selective accumulation of 131I-substrate in the pancreas and rapid clearance from most of the body organs.

Two-dimensional-quantitative structure-activity relationship studies of a novel series of diaryl furanone derivatives: An approach to design selective and effective cyclooxygenase-2 inhibitors

INTRODUCTION: Over a decade, a large number of selective cyclooxygenase-2 (COX-2) inhibitors with diverse chemical characteristics have been designed but only a few have emerged as drugs. MATERIALS AND METHODS: In the present study, two-dimensional-quantitative structure-activity relationship (2D-QSAR) studies were performed on a set of 43 novel derivatives of diaryl furanone using V-Life Molecular Design Suite (MDS 3.5) QSAR plus module using multiple linear regression (MLR) and Partial Least Square (PLS) regression methods against a COX-2 enzyme. RESULTS AND DISCUSSION: PLS method has displayed a very significant prediction results. QSAR model was generated by a training set of 33 molecules with correlation coefficient (r2) of 0.7695, cross-validated correlation coefficient (q2) of 0.5359 and F test of 23.3734. The estate contribution, chi, hydrophobic, and alignment-independent descriptors were major contributors. CONCLUSION: 2D-QSAR model result showed the positive contribution of oxygen atoms and negative contribution of rotatable bonds and distance between double bonds toward biological activity.

Alignment independent 3D-QSAR, quantum calculations and molecular docking of Mer specific tyrosine kinase inhibitors as anticancer drugs

Mer receptor tyrosine kinase is a promising novel cancer therapeutic target in many human cancers, because abnormal activation of Mer has been implicated in survival signaling and chemoresistance. 3D-QSAR analyses based on alignment independent descriptors were performed on a series of 81 Mer specific tyrosine kinase inhibitors. The fractional factorial design (FFD) and the enhanced replacement method (ERM) were applied and tested as variable selection algorithms for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. The data set was split into 65 molecules as the training set and 16 compounds as the test set. All descriptors were generated by using the GRid INdependent descriptors (GRIND) approach. After variable selection, GRIND were correlated with activity values (pIC50) by PLS regression. Of the two applied variable selection methods, ERM had a noticeable improvement on the statistical parameters of PLS model, and yielded a q2 value of 0.77, an rpred2 of 0.94, and a low RMSEP value of 0.25. The GRIND information contents influencing the affinity on Mer specific tyrosine kinase were also confirmed by docking studies. In a quantum calculation study, the energy difference between HOMO and LUMO (gap) implied the high interaction of the most active molecule in the active site of the protein. In addition, the molecular electrostatic potential energy at DFT level confirmed results obtained from the molecular docking. The identified key features obtained from the molecular modeling, enabled us to design novel kinase inhibitors.

3D-QSAR and Docking Studies of a Series ofβ-Carboline Derivatives as Antitumor Agents of PLK1

An alignment-free, three dimensional quantitative structure-activity relationship (3D-QSAR) analysis has been performed on a series ofβ-carboline derivatives as potent antitumor agents toward HepG2 human tumor cell lines. A highly descriptive and predictive 3D-QSAR model was obtained through the calculation of alignment-independent descriptors (GRIND descriptors) using ALMOND software. For a training set of 30 compounds, PLS analyses result in a three-component model which displays a squared correlation coefficient (r2) of 0.957 and a standard deviation of the error of calculation (SDEC) of 0.116. Validation of this model was performed using leave-one-out,q2looof 0.85, and leave-multiple-out. This model gives a remarkably highr2pred(0.66) for a test set of 10 compounds. Docking studies were performed to investigate the mode of interaction betweenβ-carboline derivatives and the active site of the most probable anticancer receptor, polo-like kinase protein.

Exploring QSAR studies on 4-substituted quinazoline derivatives as antimalarial compounds for the development of predictive models

Quantitative structure activity relationship (QSAR) model for antimalarial activity was developed from a set of 51 substituted quinazolines that exhibited remarkable in vitro activity against sensitive and multidrug-resistant Plasmodium falciparum malaria. 2D-QSAR was done using partial least squares method coupled with stepwise variable selection; subsequently, 3D-QSAR was carried out using stepwise variable selection k-nearest neighbor molecular field analysis (kNNMF) approach. Leave-one-out cross-validated correlation coefficient (q2) of 0.7601 and a predicted r2 for the external test (pred_r2) of 0.7108 were obtained with 3D QSAR. Experimental results revealed that the molecular weight, alignment-independent descriptors, electrostatic and steric field descriptors were significantly correlated with antimalarial activity of quinazoline derivatives. The results helped to understand the nature of substituents around quinazoline nucleus, thereby providing new guidelines for the design of novel antimalarials.

QSAR Study on a Series of Aryl Carboxylic Acid Amide Derivatives as Potential Inhibitors of Dihydroorotate Dehydrogenase (DHODH)

QSAR study was performed on a series of aryl carboxylic acid amide derivatives (62 analogs) to establish structural features required for human dihydroorotate dehydrogenase (hDHODH) inhibition. Statistical significant QSAR models were developed for the prediction of hDHODH inhibitory activity by applying MLR analysis (r2 = 0.851 and q2 = 0.795), PCR analysis (r2 = 0.713 and q2 = 0.667) and PLS analysis (r2 = 0.848 and q2 = 0.802). QSAR study emphasized the importance of topological, estate number, hydrophobic and alignment independent descriptors for the prediction of hDHODH inhibitory activity. SaasCcount descriptor suggested the presence of carbon atoms in five member aryl ring system. Positive impact of alignment independent descriptors reveals the presence of carbonyl oxygen and chloro group in this series of compounds. DistTopo signifies basic connectivity of atoms in the molecules. High degree of predictability of the proposed QSAR models offers a great potential for the design and development of potent hDHODH inhibitors.

Diverse Models for the Prediction of HIV Integrase Inhibitory Activity of Substituted Quinolone Carboxylic Acids

In the present study both classification and correlation techniques of diverse nature were successfully employed for the development of models for the prediction of human immunodeficiency virus (HIV) integrase inhibitory activity using a dataset comprising 50 analogs of quinolone carboxylic acid. The values of various molecular descriptors (MDs) for each analog in the dataset were computed using the MDS V-life science QSAR plus module. The values of other MDs which are not part of MDS V-life science were computed using an in-house computer program. A decision tree (DT) was constructed for the HIV integrase inhibitory activity to determine the importance of MDs. The DT learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10 fold) data with an accuracy of 96%. Three MDs, E-state contribution descriptor (SssOHE), molecular connectivity topochemical index ($\chi {}^{{\rm A}} $), and eccentric connectivity topochemical index ($\xi _{{\rm C}}^{{\rm C}} $), were used to develop the models using moving average analysis (MAA). The accuracy of classification of single descriptor based models using MAA was found to vary from a minimum of 96% to a maximum of 98%. The statistical significance of the models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient, and intercorrelation analysis. The widely used methods like multiple linear regression, partial least squares, and principal component regression were employed for development of correlation models. The models were generated on a training set of 36 molecules. The models had a correlation coefficient (r(2) ) of 0.86 to 0.92, significant cross validated correlation coefficient (q(2) ) of 0.79 to 0.85, F-test from 63.2 to 93.06, r(2) for external test set (pred_r(2) ) from 0.69, coefficient of correlation of predicted dataset (pred_ r(2) Se) of 0.77, and degree of freedom from 27 to 30. Alignment independent descriptors, SsOHE-index, SaaCHE index, SssCH2, and x log P were found to be the most important descriptors for the development of correlation models for the prediction of HIV integrase inhibitory activity.

The 3D-QSAR study of 110 diverse, dual binding, acetylcholinesterase inhibitors based on alignment independent descriptors (GRIND-2). The effects of conformation on predictive power and interpretability of the models

The 3D-QSAR analysis based on alignment independent descriptors (GRIND-2) was performed on the set of 110 structurally diverse, dual binding AChE reversible inhibitors. Three separate models were built, based on different conformations, generated following next criteria: (i) minimum energy conformations, (ii) conformation most similar to the co-crystalized ligand conformation, and (iii) docked conformation. We found that regardless on conformation used, all the three models had good statistic and predictivity. The models revealed the importance of protonated pyridine nitrogen of tacrine moiety for anti AChE activity, and recognized HBA and HBD interactions as highly important for the potency. This was revealed by the variables associated with protonated pyridinium nitrogen, and the two amino groups of the linker. MIFs calculated with the N1 (pyridinium nitrogen) and the DRY GRID probes in the AChE active site enabled us to establish the relationship between amino acid residues within AChE active site and the variables having high impact on models. External predictive power of the models was tested on the set of 40 AChE reversible inhibitors, most of them structurally different from the training set. Some of those compounds were tested on the different enzyme source. We found that external predictivity was highly sensitive on conformations used. Model based on docked conformations had superior predictive ability, emphasizing the need for the employment of conformations built by taking into account geometrical restrictions of AChE active site gorge.

2D-QSAR Studies of Tacrine Analogues for Acetylcholine Esterase (AChE) Inhibition using Multiple Linear Regression Analysis

Quantitative structure–activity relationship (QSAR) of tacrine analogues for acetylcholine esterase (AChE) inhibitory activity was studied using stepwise multiple linear regression (MLR). AChE activity (pIC50) of tacrine derivatives was expressed with acceptable explanation (95.5%) in Model (1) and other statistically acceptable predictive power (76.7-93.6 %) (Models 2-6). Though Model (5) was the best equation with better prediction and with a smaller standard error than other methods, it did not give a good correlation with the external training set. Model (3) showed higher correlation coefficient (61.03%) than the others. The resulting models with the given descriptors illustrate the significant role of T_N_Cl_4 & 6, T_C_O_7 and T_2_O_7 on increasing AChE activity and all other descriptors in the equations amounted to decrease in AChE activity. The QSAR studies provide useful information for design of new ligands. Keywords: 2D QSAR; Acetylcholine Esterase; Alignment independent descriptors; Alzheimers; Stepwise multiple linear regression; Tacrine

RATIONAL DESIGN OF ANTIBACTERIAL THIENOPYRIMIDINES BY 2D-QSAR STUDY

QSAR studies were performed on a set of 43 analogs of thienopyrimidine using V-Life Molecular Design Suite (MDS 3.5) QSAR plus module by using Multiple Linear Regression (MLR) and Partial Least Squares (PLS) Regression methods against a gram positive (S.aureus) and a gram negative (E.coli) bacteria. MLR method has shown a very promising prediction results in both S.aureus and E.coli. QSAR model was generated by a training set of 34 molecules with correlation coefficient (r2) of 0.9849, 0.8719, significant cross validated correlation coefficient (q2) of 0.8881, 0.7811 and F test of  40.4301, 40.4768 respectively. In the selected descriptors, alignment independent descriptors such as T_C_C_7, T_N_O_3, T_2_N_1, T_N_O_1, T_O_O_7 and T_N_Cl_4 were the most important descriptors in predicting antibacterial activity.

3D-QSPR models for predicting the enantioselectivity and the activity for asymmetric hydroformylation of styrene catalyzed by Rh–diphosphane

This paper describes the development of quantitative structure–enantioselectivity and–activity relationships for the asymmetric hydroformylation of styrene by Rh–diphosphane. We used 3D steric- and electrostatic-type interaction fields derived from DFT calculations, and generated alignment-independent descriptors using GRid INdependent Descriptor (GRIND) methodology. The obtained QSPR models showed statistical significance and predictive ability. The most predictive model for enantioselectivity was obtained using steric-type 3D fields (MSF) based on the local curvature electron density isosurface that accounts for catalyst shape (r2 = 0.92, q2 = 0.68). We obtained the most predictive model for activity using a combination of shape- and electrostatic-based 3D fields (r2 = 0.99, q2 = 0.74). The use of chemically meaningful descriptors provides insight into the factors governing catalytic activity and selectivity. The worst predicted ligand, kelliphite, showed the lowest preference for equatorial–apical coordination and low selectivity, which suggests that its intrinsic enantiotopic differentiation capacity can be lost through the occurrence of bis-equatorial paths. The selective catalysts Rh–chiraphite, –binapine, –diazaphospholane, and –yanphos showed the same pattern as Rh–binaphos, for which the origin of stereoinduction is known: the chirality at the apical site discriminates one alkene coordination path and one enantiomer. Ligands with electron withdrawing groups at phosphorus atoms such as chiraphite, kelliphite, binaphos, and diazaphospholane reduce ligand basicity and promote catalytic activity effectively. However, more complex relationships underlie the origin of activity, and the shape of the catalyst also needs to be considered such as for the Ph-BPE ligand. Comparison with previous studies suggests that reduction of the steric hindrance at the reaction centre which favors alkene coordination and insertion would also promote catalytic activity.

2D and 3D QSAR study on amino nicotinic acid and isonicotinic acid derivatives as potential inhibitors of dihydroorotate dehydrogenase (DHODH)

Dihydroorotate dehydrogenase (DHODH) is a central enzyme of pyrimidine biosynthesis which catalyzes oxidation of dihydroorotate (DHO) to orotate (ORO). DHODH inhibitors are considered as promising targets for the development of antiproliferative, antiparasitic, and immunosuppressive drugs. The best 2D QSAR model for the prediction of human dihydroorotate dehydrogenase (hDHODH) inhibitory activity was obtained by applying MLR giving r2 = 0.834 and q2 = 0.756, PCR giving r2 = 0.833 and q2 = 0.756, and PLS giving r2 = 0.864 and q2 = 0.786. 2D QSAR studies reveal the importance of alignment independent descriptors for predicting hDHODH inhibitory activity. The 3D QSAR study was performed by comparative molecular field analysis (CoMFA) to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The best CoMFA model obtained for the training set was statistically significant with cross-validated coefficients (q2) of 0.630 and conventional coefficients (r2) of 0.949. The CoMFA contour maps suggest some important structural features-like electronegative substituents at biphenyl ring system for strong inhibitory activity. We believe that these results are helpful in design of more potent and selective hDHODH inhibitors.

Antiproliferative activity of aroylacrylic acids. Structure-activity study based on molecular interaction fields

Antiproliferative activity of 27 phenyl-substituted 4-aryl-4-oxo-2-butenoic acids (aroylacrylic acids) toward Human cervix carcinoma (HeLa), Human chronic myelogenous leukemia (K562) and Human colon tumor (LS174) cell lines in vitro are reported. Compounds are active toward all examined cell lines. The most active compounds bear two or three branched alkyl or cycloalkyl substituents on phenyl moiety having potencies in low micromolar ranges. One of most potent derivatives arrests the cell cycle at S phase in HeLa cells. The 3D QSAR study, using molecular interaction fields (MIF) and derived alignment independent descriptors (GRIND-2), rationalize the structural characteristics correlated with potency of compounds. Covalent chemistry, most possibly involved in the mode of action of reported compounds, was quantitatively accounted using frontier molecular orbitals. Pharmacophoric pattern of most potent compounds are used as a template for virtual screening, to find similar ones in database of compounds screened against DTP-NCI 60 tumor cell lines. Potency of obtained hits is well predicted.

QSAR studies of some substituted imidazolinones angiotensin II receptor antagonists using Partial Least Squares Regression (PLSR) method based feature selection

The Quantitative structure–activity relationship (QSAR) analyses were carried out for a series of imidazolinones as nonpeptide angiotensin II receptor antagonists to find out the structural requirements of their antihypertensive activities. Partial Least Squares Regression (PLSR) method coupled was applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant best model with high correlation coefficient (r2 = 0.9031) was selected for further study. The model was further validated by means of crossed squared correlation coefficient (q2 = 0.8652 and pred_r2 = 0.8261) which shows the model has good predictive ability, higher value of F statics 66.831 also validates significance of model, Degree of freedom20. The physicochemical descriptor SssCH2count and alignment-independent descriptors T_N_O_6, T_2_Cl_6 and Chi5 were found to show significant correlation with biologic activity in imidazolinones. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the antihypertensive new design molecules.

2D and 3D-QSAR study on 4-anilinoquinozaline derivatives as potent apoptosis inducer and efficacious anticancer agent

BackgroundApoptosis is known as programmed cell death that plays an important role in tumor biology.MethodsIn this study, apoptosis-inducing activity is predicted by using a QSAR modeling approach for a series of 4-anilinoquinozaline derivatives. 2D-QSAR model for the prediction of apoptosis-inducing activity was obtained by applying multiple linear regression giving r2 = 0.8225 and q2 = 0.7626, principal component regression giving r2 = 0.7539 and q2 = 0.6669 and partial least squares giving r2 = 0.8237 and q2 = 0.6224.ResultsQSAR study revealed that alignment-independent descriptors and distance-based topology index are the most important descriptors in predicting apoptosis-inducing activity. 3D-QSAR study was performed using k-nearest neighbor molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Three different kNN-MFA 3D-QSAR methods (SW-FB, SA, and GA) were used for the development of models and tested successfully for internal (q2 > 0.62) and external (predictive r2 > 0.52) validation criteria. Thus, 3D-QSAR models showed that electrostatic effects dominantly determine the binding affinities.ConclusionsThe QSAR models developed in this study would be useful for the development of new apoptosis inducer as anticancer agents.

2D QSAR Studies on a Series of Quinazoline Derivatives as Tyrosine Kinase (EGFR) Inhibitor: An Approach to Design Anticancer Agents

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are known for its role in cancer. QSAR studies were performed on a set of 137 analogs of quinazoline using MDS vlife science QSAR plus module by using Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares (PLS) Regression methods. Among these, MLR method has shown a very promising result as compared to other two methods and a QSAR model was generated by a training set of 100 molecules with correlation coefficient (r2) of 0.884, significant cross validated correlation coefficient (q2) of 0.800, F test of 39.5149, r2 for external test set (pred_r2) 0.5902, coefficient of correlation of predicted data set (pred_r2se) 0.7438 and degree of freedom 83. In the selected descriptors, alignment independent descriptors T_2_C_5 (11.74%) and T_2_O_7 (-11.27%) are the most important descriptors in predicting EGFR inhibitory activity. Electron withdrawing group at anilino quinazolines enhances the activity as evident by positive value of T_F_Cl_4 (2.07%). In addition, for quinazoline substituents, estate contribution descriptors, SaaCHE – index and Ssss NE-index have a large deactivating effect. Keywords: Quinazoline, Tyrosine kinase (EGFR), Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS)