K-nearest Neighbor Molecular Field Analysis Research Articles

In-silico Studies of Heterocyclic Benzoxazole Derivatives as an Anticancer Agent: Molecular Docking, 2D and 3D QSAR

In-silico molecular docking studies and QSAR study of benzoxazole derivatives synthesized by Kakkar et al. was done. Comparative studies of docking of 5-flurouracil and 20 compounds revealed considerable interactions, indicating the affinity of newly synthesized compounds for thymidylate synthase. The statistically significant 2D-QSAR models were developed using a molecular design suite (VLifeMDS 4.6). The study was performed with 20 compounds (data set) using sphere exclusion (SE) algorithm, random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression [MLR] methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by k-Nearest Neighbor Molecular Field Analysis (kNN-MFA), investigating the substitutional requirements for the favorable anticancer activity against HCT 116 cell line and providing useful information in the characterization and differentiation of their binding sites. The results may be useful for further designing benzoxazole derivatives as anticancer agents prior to synthesis.

3D QSAR study on substituted 1, 2, 4 triazole derivatives as anticancer agents by kNN MFA approach

Background and objectivesResearchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinalvalue. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method. MethodsUsing the molecular design suite, a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation. ResultsThe most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047––0.0780––0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities. ConclusionThe good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

2D & 3D-QSAR Studies on a Series of Quinoline-Amino-piperidine Derivatives as Potent Mycobacterium DNA-Gyrase-B Inhibitors

Introduction: Mycobacterium tuberculosis is a familiar infectious bacillus that causes tuberculosis, which primarily affects the lungs and the spinal cord. To combat the growing difficulties in treating MTB, it is necessary to create safe medications with novel mechanisms of action.
 Objective: To design and develop some novel quinolone-amino piperidine derivatives with potent mycobacterium DNAgyraseB inhibitory using the QSAR technique.
 Methods: Multiple linear regression (MLR), partial least squares (PLS), and k-nearest neighbour molecular field analysis ((kNN-MFA) were utilised in the development of 2D and 3D-QSAR models, respectively; these models were then validated. 
 Results: The recently developed 2D-QSAR model can explain 85.07% (r2 = 0.8507) of the total variance incorporated into the training set. In addition, the model has an internal prediction capacity (q2) of 77.65% and an external prediction capacity (pred r2) of 83.64%, respectively. The F test confirms that the likelihood of the model failing is extremely low. The 3D-QSAR model explains the values of k (2), q2 = 0.5707, pred r2 = 0.7843, q2 se = 0.3167, and pred r2 se = 0.3111. This demonstrates that the QSAR equation obtained in that way is statistically significant and that the model has a predictive capacity of 78.43%.
 Conclusion: The robustness of the developed 2D or 3D-QSAR models provides the necessary information and is expected to provide an excellent option for drug design.

Synthesis, Characterization and Evaluation of Some Novel 2-Mercaptobenzothiazole Derivatives: In Silico and Experimental Approach

Recently, QSAR technique and molecular docking have become widespread in designing new series of compounds, depending upon the different models developed, before actually synthesizing and testing them. Due to the diverse activity profile of substituted Benzothiazole, it has become a pharmacophore of interest. The main motive of the study was to design some novel 2- Mercaptobenzothiazole molecules with potential anti-inflammatory effect. Thus, in order to get a better insight into the structural features of 2- Mercaptobenzothiazole, a series of analogues were taken from the literature and 2D, 3D QSAR models were developed and molecular docking and ADME studies were also performed. For 2D QSAR study, stepwise forwarding variable selection method, coupled with Partial Least Square (SWF-PLS) regression-based method, was used to generate the best 2D model giving r2= 0.9425, q2=0.9018, F-value=90.0118, pred_r2=0.8976. In contrast, the forward stepwise variable selection k-nearest neighbour molecular field analysis approach was used to generate best 3D QSAR model (q2=0.8171 and pred_r2=0.7006). Docking studies were performed on 29 derivatives of 2-Mercaptobenzothiazoles. The ligands were prepared and docked against protein S. aureus TYrRS with a resolution of 3.2Å. The three derivatives K6, K18 and K20 were found to have the best docking scores (-48.44, -47.46, -48.52). Further, synthesis of these three derivatives was carried out. Spectral characterization was carried out by IR, 1H NMR and 13C NMR and all the structures were found to be in agreement with the data obtained. In vitro anti-inflammatory evaluation of these compounds was carried out by following the protein denaturation method and HRBC membrane stabilization method. Compound K20 displayed potential anti-inflammatory activity, which may be attributed to the electronegative group on the phenyl ring substituted on the pyrazole part of the pharmacophore.

A Combined Approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and In silico ADME/Tox Prediction of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one Analogs to Identify Potential Reverse Transcriptase Inhibitor: Anti-HIV Agents.

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, costeffective, and high throughput screening method to identify potential reverse transcriptase inhibitors. A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square is associated with the Sphere Exclusion method (PLS-SE), having r2 and q2 values 0.9509 and 0.8038, respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant, which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-crossvalidated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed, which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties. The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

QSAR STUDIES ON HUMAN CARBONIC ANHYDRASE II INHIBITORS

Carbonic anhydrase II is one of the forms of human α carbonic anhydrases which are ubiquitous metalloenzymes that catalyze inter-conversion of carbon dioxide and water to bicarbonate and proton, overexpression of which leads to disorders such as glaucoma. 2D and 3D Quantitative Structure Activity Relationship studies were carried out on previously synthesized series of sulfanilamide derivatives by VLife MDS software using stepwise variable, multi-linear regression and k-nearest neighbor molecular field analysis methods. 2D-QSAR model depicts contribution of halogens (such as chlorine and fluorine), methylene and oxygen atoms to inhibition of human carbonic anhydrases II activity. Using k-nearest neighbor molecular field analysis method two 3D-QSAR models (model A and B) were generated from which model A was found to be the best validated model with q2 (0.9494), pred_r2 (0.7367) and q2 _ se (0.2037). It displayed the fact that the inhibitory action of sulfanilamide derivatives against human carbonic anhydrases II is influenced by hydrophobicity and electro positivity.

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead.

QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents

The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity. We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods. 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive. Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

2D and 3D QSAR Studies of Saponin Analogues as Antifungal Agents against Candida albicans

: Objectives: Present communication deals with two- and three-dimensional (2D and 3D) QSAR studies of twenty saponin analogue for antifungal activity against Candida albicans. Methods: The 2D-QSAR model for the prediction was obtained by applying Multiple Linear Regression (MLR) method, giving r2 = 0.8551 and q2 = 0.7717 and Partial Least Squares (PLS) method, giving r2 = 0.8551 and q2 = 0.7717. 3D-QSAR study was performed using the stepwise variable selection k-nearest neighbour molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Two different kNN-MFA methods (SA and GA) were used for the building of 3D-QSAR models. Results: The best model shows interesting result in terms of internal (q2 > 0.62) and external (predictive r2 > 0.52) predictivity for training and test set. Conclusion: Thus, QSAR models showed that hydrophobic and electrostatic effects dominantly determine the binding affinities. Hence the QSAR models proposed in this work would be further useful for development of new antifungal agents from medicinal plants and can help in the design of novel potent molecule.Key words: Saponin, QSAR, MLR, PLS, Candida.

THREE-DIMENSIONAL QSAR MODELING BENZIMIDAZOLE ANALOGUES USING THE K-NEAREST NEIGHBOR METHOD

We undertook the three-dimensional (3D) QSAR studies of a series of benzimidazole analogues to elucidate the structural properties required for angiotensin II. The 3D-QSAR studies were performed using the stepwise, simulated annealing (SA) and genetic algorithm (GA) selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2 = 0.8216 and a pred_r2 = 0.7852 were obtained. The 3D QSAR model is expected to provide a good alternative to predict the biological activity prior to synthesis as antihypertensive agents.

3D QSAR AND DOCKING STUDY OF INDOLE DERIVATIVES AS SELECTIVE COX-2 INHIBITORS

Objective: Non-steroidal anti-inflammatory agents (NSAIDs) continue to be one of the most widely used groups of therapeutic agents. QSAR (quantitative structure-activity relationship) approach is a very useful and widespread technique for drug design. 3D QSAR facilitates evaluation of three-dimensional molecular fields around molecules and generates a relationship of these fields' values with the activity.
 Methods: 3D QSAR study was performed on selected twenty-four compounds from synthesized indole derivatives using the stepwise variable selection k-nearest neighbor (kNN) molecular field analysis approach for indicating the contribution of the steric and electronic field for activity. The docking study was performed to further confirm the binding affinity of synthesized molecules (ligands) to COX-2 enzyme as well as to study binding nature.
 Results: Statistically significant model was generated using VLife Molecular Design Suite 3.5 software with cross-validated correlation coefficient q2 of 0.9461 and high predictive correlation coefficient (Pred_r2) of 0.8782 indicating that the model is robust. The results of docking study suggest that the synthesized compounds have a comparable binding affinity with the COX-2 enzyme.
 Conclusion: The present study may prove to be helpful in the development and optimization of existing indole derivatives as anti-inflammatory agents with selective COX-2 inhibition.

INSIGHT INTO THE STRUCTURAL REQUIREMENTS OF SULFONYL DERIVATIVES BASED ON TWO AND THREE-DIMENSIONAL DESCRIPTORS: QSAR STUDIES

The present work provides the rationale to the changes in the structure to have more potent analogs sulfonyl derivatives were reported to possess potent activity for the angiotensin AT1 receptor. We report here 2D QSAR and k-nearest neighbor molecular field analysis based model for sulfonylureas compounds as AT1 receptor. The here k-nearest neighbor contour plots provided further understanding of the relationship between structural features of substituted sulfonyl derivatives and their activities which should be applicable to design newer potential AT1 receptor.

2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models.
 The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors.
 Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA).
 Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant.
 The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds.
 Conclusion: The developed 2D and 3D QSAR models have shown good r2 and q2 values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties.
 The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

Computational investigation of binding mechanism of substituted pyrazinones targeting corticotropin releasing factor-1 receptor deliberated for anti-depressant drug design

In spite of various research investigations towards anti-depressant drug discovery program, no one drug has not yet launched last 20 years. Corticotropin-releasing factor-1 (CRF-1) is one of the most validated targets for the development of antagonists against depression, anxiety and post-traumatic stress disorders. Various research studies suggest that pyrazinone based CRF-1 receptor antagonists were found to be highly potent and efficacious. In this research investigation, we identified the pharmacophore and binding pattern through 2D and 3D-QSAR and molecular docking respectively. Molecular dynamics studies were also performed to explore the binding pattern recognition. We establish the relationship between activity and pharmacophoric features to design new potent compounds. The best 2D-QSAR model was generated through multiple linear regression method with r2 value of 0.97 and q2 value of 0.89. Also 3D-QSAR model was obtained through k-nearest neighbor molecular field analysis method with q2 value of 0.52 and q2_se value of 0.36. Molecular docking and binding energy were also evaluated to define binding patterns and pharmacophoric groups, including (i) hydrogen bond with residue Asp284, Glu305 and (ii) π–π stacking with residue Trp9. Compound 11i has the highest binding affinity compared to reference compounds, so this compound could be a potent drug for stress related disorders. Most of the compounds, including reference compounds were found within acceptable range of physicochemical parameters. These observations could be provided the leads for the design and optimization of novel CRF-1 receptor antagonists.Communicated by Ramaswamy H. Sarma

Insight into structural requirements of antiamoebic flavonoids: 3D-QSAR and G-QSAR studies.

Plant-based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D-QSAR analysis using k-nearest neighbour molecular field analysis via stepwise forward-backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q2 =0.64 and pred_r2 =0.56). Also, a group-based QSAR (G-QSAR) model was developed based on partial least squares regression combined with stepwise forward-backward variable selection method. It gave best parametric results (r2 =0.74, q2 =0.56 and pred_r2 =0.54) which implied that the model is highly predictive. 3D-QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G-QSAR provided site-specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.

PREDICTIVE QSAR MODELING OF PYRIDAZINYL DERIVATIVES USING K-NEAREST NEIGHBOR AND PHARMACOPHORE APPROACH

This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.

DEVELOPMENT OF A ROBUST QSAR MODEL OF ANGIOTENSIN RECEPTOR REVEALS A K NEAREST NEIGHBOR APPLICABLE TO DIVERSE SCAFFOLDS

Quantitative structure–activity relationship (QSAR) studies were performed on quinazolinone analogues for prediction of antihypertensive activity. The best significant 2D-QSAR model having r2 = 0.8118 and pred_r2 = 0.7428 was developed by stepwise-partial least square method. k-nearest neighbor molecular field analysis was used to construct the best 3D-QSAR model, showing good correlative and predictive capabilities in terms of q2 = 0.7388 and pred_r2 = 0.6983. Results reveal that the 2D-QSAR studies signify positive contribution of SssOE index and SsCH3 count towards the biological activity. The results have showed that electronegative groups are necessary for activity and halogen, bulky, less bulky groups in quinazolinones nucleus enhanced the biological activity. The information rendered by 2D- and 3D-QSAR models may lead to a better understanding of structural requirements of substituted quinazolinones derivatives and also aid in designing novel potent antihypertensive molecules.

Possible Binding Mode Analysis of Pyrazolo-triazole Hybrids as Potential Anticancer Agents through Validated Molecular Docking and 3D-QSAR Modeling Approaches

Background: There has been a growing interest of pharmacophore hybridization in anticancer drug discovery that may be utilized for designing new potential lead candidates against multiple targets that may exhibit synergetic activity. Pyrazole and 1,2,3-triazole nucleus are amongst the most important ones. Keywords: ADMET, anticancer agents, druggability prediction, k-Nearest neighbor molecular field analysis, molecular docking, pyrazolo-triazole hybrids.

Possible anticancer agents: synthesis, pharmacological activity, and molecular modeling studies on some 5-N -Substituted-2-N-(substituted benzenesulphonyl)-L(+)Glutamines

On the basis of our earlier work, fortyone 5-N-substituted-2N-(substituted benzenesulphonyl)-L(+)glutamines were synthesized and screened for cancer cell inhibitory activity. The best active compounds showed 91% tumor cell inhibition, whereas other three compounds showed more than 80% inhibition. Two-dimensional quantitative structure–activity relationship modeling and three-dimensional quantitative structure–activity relationship k-nearest neighbor molecular field analysis studies were done to get an insight into structural requirements toward further improved anticancer activity. Considering the fact that these compounds are competitive inhibitors of glutaminase, a molecular docking study followed by molecular dynamic simulation analysis were performed. The work may help to develop new anticancer agents.

Structural Requirements for some 3-amino-N-substituted-4-(substituted phenyl) Butanamides as Dipeptidyl Peptidase-IV Inhibitors Using 3D-QSAR and Molecular Docking Approaches

Dipeptidyl peptidase-IV is well thought out as one of the key targets for treatment of type 2 diabetes mellitus and diverse scaffolds have proven effective in designing novel dipeptidyl peptidase-IV inhibitors. To this end, three dimensional quantitative structure activity relationship analysis and molecular docking studies were performed on a set of 3-amino-N-substituted- 4-(substituted phenyl) butanamides to explore the structural requirements for dipeptidyl peptidase-IV inhibitory activity using k-nearest neighbour molecular field analysis and AutoDock Vina, respectively. The compounds were arbitrarily assigned to active, moderately active and less active classes according to their biological activities. The most significant k-nearest neighbour model exhibited internal cross validation coefficient (q2) and external cross validation coefficient (pred_r2) as 0.67 and 0.82, respectively while the best partial least squares regression model showed 70% internal and 77% external predictability. Subsequent rigorous external validation through q2(F2), q2(F3), concordance correlation coefficient and mean absolute error provided further confidence in the predictability of both the models. Apropos the docking results, the compounds from active, moderately active and less active classes exhibited different binding patterns. Whereas the active compounds such as 12x and 12v occupied a similar space in the active site cavity as the crystallographic ligand (sitagliptin) (PDBID:1X70); the binding modes of compounds from moderately active and inactive classes were distinct from the latter. The findings of this study can provide an impetus for design of prospective potent dipeptidyl peptidase-IV inhibitors.