Three-dimensional quantitative structure–activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA

Gueto, Carlos; Ruiz, José Luis; Torres, Juan E.; Méndez, Jefferson; Vivas‐Reyes, Ricardo

doi:10.1016/j.bmc.2007.11.053

Cited by 20 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results satisfy the statistical validity criteria and allow for the construction of a significant QSAR model. These values indicated that the CoMSIA model had a better conventional statistical correlation and a fair predictive ability (21). Within CoMFA model, the corresponding distributions of steric and electrostatic fields were 0.517 and 0.483, respectively, which indicated that steric field played more important role in inhibitory activity.…”

Section: Resultsmentioning

confidence: 99%

Molecular Docking, 3D‐QSAR Studies, and In Silico ADME Prediction of p‐Aminosalicylic Acid Derivatives as Neuraminidase Inhibitors

et al. 2011

View full text Add to dashboard Cite

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug design and discovery. To develop novel potent neuraminidase inhibitors (NAI), Surflex-Dock was employed to dock 40 hydrophobic p-aminosalicylic acid derivatives into the active site of NA. The 3D-quantitative structure-activity relationship studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 40 molecules. Both CoMFA (q(2) = 0.628, r(2) = 0.697) and CoMSIA (q(2) = 0.746, r(2) = 0.816) gave reasonable results. A preliminary pharmacokinetic profile of these NAI was also performed on the basis of Volsurf predictions. The results obtained from this study will be useful in the design of novel potent NAI.

show abstract

Section: Resultsmentioning

confidence: 99%

Molecular Docking, 3D‐QSAR Studies, and In Silico ADME Prediction of p‐Aminosalicylic Acid Derivatives as Neuraminidase Inhibitors

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Thaimattam et al [18] reported 3-D QSAR studies on c-Src kinase inhibitors and docking analyses. Gueto et al [19] conducted CoMFA and CoM-SIA analysis study of a series of benzotriazine Src inhibitors. In addition, a combination of 2-D and 3-D QSAR analysis to a series of pyrido [2,3-d]pyrimidine derivatives has been carried out by Caballero et al [20] to correlate cSrc tyrosine kinases inhibition, where less attention has been paid on 2-D analysis.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Replacement Method‐based Quantitative Structure–Activity Relationship Modeling and Support Vector Machine Classification of 4‐Anilino‐3‐quinolinecarbonitriles as Src Kinase Inhibitors

et al. 2009

View full text Add to dashboard Cite

Quantitative Structure -Activity Relationship (QSAR) analyses have been carried out for a set of 4-anilino-3-quinolinecarbonitriles. Considering simplicity and interpretability, Src kinase-inhibiting activity of these compounds expressed in log units have been modeled by Multiple Linear Regression (MLR) analysis combined with various variable selection approaches, including Forward Selection (FS), Genetic Algorithm (GA), Simulated Annealing (SA), and Enhanced Replacement Method (ERM), based on descriptors generated by E-Dragon software. Performances of these models are rigorously validated by Leave-One-Out Cross-Validation (LOOCV), five-fold Cross-Validation (5-CV), and external validation. The ERM -MLR model is much better than other models, with R 2 ¼ 0.854 and R 2 pred ¼ 0.840. Robustness and predictive ability of this model are prudently evaluated. Moreover, another classification analysis using Fisher Linear Discriminant Analysis (FLDA) and Support Vector Machine (SVM) is also developed with the aim of dissecting the most significant factors that lead to the activity difference between highly active compounds and those not so active. The 5-CV and external validation prediction accuracy reached 95.00 and 93.75% for the SVM-based model, respectively. The resulting models could act as an efficient strategy for estimating the Src-inhibiting activity of novel 4-anilino-3-quinolinecarbonitriles and provide some insight into the structural features related to the biological activity of these compounds.

show abstract

“…A number of QSAR studies have been reported for benzotriazines, where different molecular descriptors are used for encoding chemical and structural features of molecules (Gueto et al, 2008;Lv et al, 2008;Ş ahin et al, 2011;Tintori et al, 2009). QSAR analysis widely uses descriptors calculated on the basis of molecular graphs (Goulon et al, 2007;Ivanciuc 2000Ivanciuc , 2013.…”

Section: Introductionmentioning

confidence: 99%

QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method

Toropov

Veselinović

et al. 2014

Med Chem Res

View full text Add to dashboard Cite

A series of 53 1,2,4-benzotriazines as inhibitors of the sarcoma family of protein tyrosine kinases have been studied. The Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships for appropriate inhibition activity. The QSAR models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system. Three various splits into training and test sets have been examined. The statistical quality of all build models is very good. The best calculated model had following statistical parameters: r 2 = 0.9843, q 2 = 0.9831 for the training set and r 2 = 0.9488, q 2 = 0.9300 for the test set. The structural indicators (alerts) for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new potential sarcoma inhibitor derivatives has been presented by using defined structural alerts.

show abstract

Three-dimensional quantitative structure–activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA

Cited by 20 publications

References 34 publications

Molecular Docking, 3D‐QSAR Studies, and In Silico ADME Prediction of p‐Aminosalicylic Acid Derivatives as Neuraminidase Inhibitors

Molecular Docking, 3D‐QSAR Studies, and In Silico ADME Prediction of p‐Aminosalicylic Acid Derivatives as Neuraminidase Inhibitors

Enhanced Replacement Method‐based Quantitative Structure–Activity Relationship Modeling and Support Vector Machine Classification of 4‐Anilino‐3‐quinolinecarbonitriles as Src Kinase Inhibitors

QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method

Contact Info

Product

Resources

About