scholarly journals Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Ying-Hsin Chang ◽  
Jun-Yan Chen ◽  
Chiou-Yi Hor ◽  
Yu-Chung Chuang ◽  
Chang-Biau Yang ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Inhibitory Activity ◽  
Estrogen Receptor Alpha ◽  
Computational Study ◽  
Three Dimensional ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Support Vector ◽  
Human Estrogen Receptor ◽  
Qsar Models

Human estrogen receptor (ER) isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ERα. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ERα.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

Chemometric Modeling of Structurally Diverse Carbamates for the Inhibition of Acetylcholinesterase (AChE) Enzyme in Alzheimer's Disease

2020 ◽  
Vol 5 (3) ◽  
pp. 6-60 ◽  
Author(s):  
Vinay Kumar ◽  
Achintya Saha
Keyword(s):  
Inhibitory Activity ◽  
Enzyme Inhibitors ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
2D Qsar ◽  
Validation Parameters ◽  
Acetylcholinesterase Enzyme ◽  
Pharmacophore Models

In this research, we have developed two-dimensional quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (GQSAR) models employing a dataset of 78 carbamate derivatives (acetylcholinesterase enzyme inhibitors). The developed models were validated using various stringent validation parameters. From the insights obtained from the developed 2D-QSAR and GQSAR models, we have found that the structural features appearing in the models are responsible for the enhancement of the inhibitory activity against the AChE enzyme. Furthermore, we have performed the pharmacophore modeling to unveil the structural requirements for the inhibitory activity. Additionally, molecular docking studies were performed to understand the molecular interactions involved in binding, and the results are then correlated with the requisite structural features obtained from the QSAR and pharmacophore models.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Nonlinear Models ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Study ◽  
Stability Robustness ◽  
Qsar Models ◽  
Leave One Out

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

The transcriptional activities and cellular localization of the human estrogen receptor alpha are affected by the synonymous Ala87 mutation

2014 ◽  
Vol 143 ◽  
pp. 99-104 ◽  
Author(s):  
Tamara Fernández-Calero ◽  
Soledad Astrada ◽  
Álvaro Alberti ◽  
Sofía Horjales ◽  
Jean Francois Arnal ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Cellular Localization ◽  
Estrogen Receptor Alpha ◽  
Receptor Alpha ◽  
Human Estrogen Receptor

scholarly journals Quantitative Structure-Activity Relationship of Various Endogenous Estrogen Metabolites for Human Estrogen Receptor α and β Subtypes: Insights into the Structural Determinants Favoring a Differential Subtype Binding

Endocrinology ◽  
2006 ◽  
Vol 147 (9) ◽  
pp. 4132-4150 ◽  
Author(s):  
Bao Ting Zhu ◽  
Gui-Zhen Han ◽  
Joong-Youn Shim ◽  
Yujing Wen ◽  
Xiang-Rong Jiang
Keyword(s):  
Estrogen Receptor ◽  
Binding Affinity ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structural Determinants ◽  
Structure Activity ◽  
Preferential Binding ◽  
Human Estrogen Receptor ◽  
Endogenous Estrogen

To search for endogenous estrogens that may have preferential binding affinity for human estrogen receptor (ER) α or β subtype and also to gain insights into the structural determinants favoring differential subtype binding, we studied the binding affinities of 74 natural or synthetic estrogens, including more than 50 steroidal analogs of estradiol-17β (E2) and estrone (E1) for human ERα and ERβ. Many of the endogenous estrogen metabolites retained varying degrees of similar binding affinity for ERα and ERβ, but some of them retained differential binding affinity for the two subtypes. For instance, several of the D-ring metabolites, such as 16α-hydroxyestradiol (estriol), 16β-hydroxyestradiol-17α, and 16-ketoestrone, had distinct preferential binding affinity for human ERβ over ERα (difference up to 18-fold). Notably, although E2 has nearly the highest and equal binding affinity for ERα and ERβ, E1 and 2-hydroxyestrone (two quantitatively predominant endogenous estrogens in nonpregnant woman) have preferential binding affinity for ERα over ERβ, whereas 16α-hydroxyestradiol (estriol) and other D-ring metabolites (quantitatively predominant endogenous estrogens formed during pregnancy) have preferential binding affinity for ERβ over ERα. Hence, facile metabolic conversion of parent hormone E2 to various metabolites under different physiological conditions may serve unique functions by providing differential activation of the ERα or ERβ signaling system. Lastly, our computational three-dimensional quantitative structure-activity relationship/comparative molecular field analysis of 47 steroidal estrogen analogs for human ERα and ERβ yielded useful information on the structural features that determine the preferential activation of the ERα and ERβ subtypes, which may aid in the rational design of selective ligands for each human ER subtype.

scholarly journals Network-based piecewise linear regression for QSAR modelling

2019 ◽  
Vol 33 (9) ◽  
pp. 831-844
Author(s):  
Jonathan Cardoso-Silva ◽  
Lazaros G. Papageorgiou ◽  
Sophia Tsoka
Keyword(s):  
Virtual Screening ◽  
Linear Regression ◽  
Predictive Accuracy ◽  
Piecewise Linear ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Piecewise Linear Regression ◽  
Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

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