QSAR and Lead Optimization

2019 ◽  
pp. 80-100
Author(s):  
N. Ramalakshmi ◽  
S. Arunkumar ◽  
Sakthivel Balasubramaniyan
Keyword(s):  
Biological Activity ◽  
Regression Analysis ◽  
Drug Design ◽  
Physicochemical Properties ◽  
Statistical Methods ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Statistical Parameters ◽  
Design And Optimization ◽  
Genetic Function

There are many diseases for which suitable drugs have not been identified. As the population increases and the environment gets polluted, new infections are reported. Random screening of synthesized compounds for biological activity is time consuming. QSAR has a prominent role in drug design and optimization. It is derived from the correlation between the physicochemical properties and biological activity. QSAR equations are generated using statistical methods like regression analysis and genetic function approximation. Both 2D parameters and 3D parameters are involved in generating the equation. Among several QSAR equations generated, the best ones are selected based on statistical parameters. Validation techniques usually verify the predictive power of generated QSAR equations. Once the developed QSAR model is validated to be good, the results of that model may be applied to predict the biological activity of newer analogues. This chapter illustrates the various steps in QSAR and describes the significance of statistical parameters and software used in QSAR.

A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines toward α1Adrenoceptors

2006 ◽  
Vol 46 (3) ◽  
pp. 1466-1478 ◽  
Author(s):  
Laura Maccari ◽  
Matteo Magnani ◽  
Giovannella Strappaghetti ◽  
Federico Corelli ◽  
Maurizio Botta ◽  
...  
Keyword(s):  
Function Approximation ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Genetic Function

CoMFA, CoMSIA and HQSAR Analysis of 3-aryl-3-ethoxypropanoic Acid Derivatives as GPR40 Modulators

2020 ◽  
Vol 17 (1) ◽  
pp. 100-118
Author(s):  
Krishna A. Gajjar ◽  
Anuradha K. Gajjar
Keyword(s):  
Biological Activity ◽  
Structural Information ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Negative Contribution

Background: Human GPR40 receptor, also known as free fatty-acid receptor 1, is a Gprotein- coupled receptor that binds long chain free fatty acids to enhance glucose-dependent insulin secretion. In order to improve the resistance and efficacy, computational tools were applied to a series of 3-aryl-3-ethoxypropanoic acid derivatives. A relationship between the structure and biological activity of these compounds, was derived using a three-dimensional quantitative structure-activity relationship (3D-QSAR) study using CoMFA, CoMSIA and two-dimensional QSAR study using HQSAR methods. Methods: Building the 3D-QSAR models, CoMFA, CoMSIA and HQSAR were performed using Sybyl-X software. The ratio of training to test set was kept 70:30. For the generation of 3D-QSAR model three different alignments were used namely, distill, pharmacophore and docking based alignments. Molecular docking studies were carried out on designed molecules using the same software. Results: Among all the three methods used, Distill alignment was found to be reliable and predictive with good statistical results. The results obtained from CoMFA analysis q2, r2cv and r2 pred were 0.693, 0.69 and 0.992 respectively and in CoMSIA analysis q2, r2cv and r2pred were 0.668, 0.648 and 0.990. Contour maps of CoMFA (lipophilic and electrostatic), CoMSIA (lipophilic, electrostatic, hydrophobic, and donor) and HQSAR (positive & negative contribution) provided significant insights i.e. favoured and disfavoured regions or positive & negative contributing fragments with R1 and R2 substitutions, which gave hints for the modifications required to design new molecules with improved biological activity. Conclusion: 3D-QSAR techniques were applied for the first time on the series 3-aryl-3- ethoxypropanoic acids. All the models (CoMFA, CoMSIA and HQSAR) were found to be satisfactory according to the statistical parameters. Therefore such a methodology, whereby maximum structural information (from ligand and biological target) is explored, gives maximum insights into the plausible protein-ligand interactions and is more likely to provide potential lead candidates has been exemplified from this study.

scholarly journals A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines Toward α1 Adrenoceptors.

ChemInform ◽  
2006 ◽  
Vol 37 (31) ◽  
Author(s):  
Laura Maccari ◽  
Matteo Magnani ◽  
Giovannella Strappaghetti ◽  
Federico Corelli ◽  
Maurizio Botta ◽  
...  
Keyword(s):  
Function Approximation ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Genetic Function

QSAR studies for the computational prediction of HMG-CoA reductase inhibitors by genetic function approximation technique

2013 ◽  
Vol 91 (4) ◽  
pp. 263-274 ◽  
Author(s):  
Mohamed K. Awad ◽  
Eman A. El-Bastawissy ◽  
Faten M. Atlam
Keyword(s):  
Biological Activity ◽  
Correlation Coefficient ◽  
Function Approximation ◽  
Approximation Technique ◽  
Genetic Function Approximation ◽  
Qsar Study ◽  
Genetic Function ◽  
2D Qsar ◽  
Qsar Models ◽  
Coa Reductase

Two-dimensional quantitative structure−activity relationship (2D-QSAR) models are useful in understanding how chemical structure is related to the biological activity of natural and synthetic chemicals. Also, they could be usefully employed for designing newer and better therapeutics. A 2D-QSAR study was performed for 52 compounds of a series of thiophenyl quinolines and α-asarone derivatives as potential hypocholesterolemic inhibitors using different types of physicochemical descriptors, which correlated significantly with the activity. Linear QSAR models were developed using multiple linear regression, where the genetic algorithm (genetic function approximation technique) was adopted for selecting the most appropriate descriptors. The results are discussed on the basis of regression data and the cross-validation technique. Model A is the best 2D-QSAR model describing the inhibition efficiency of HMG-CoA reductase with cross-validated squared correlation coefficient (Q 2 = 0.700) and the squared correlation coefficient (R 2 = 0.752), which is able to describe 70% of the variance in the experimental activity. The good agreement between the experimental and the predicted values of pIC50 (micromoles per litre) (R = 0.876) confirms the reliability and the predictability of the proposed model. The results obtained from the present QSAR study explained the importance of the electronic, structural, spatial, and electrotopological descriptors in enhancing the biological activity of the investigated inhibitors.

scholarly journals Synthetic auxin-based double salt ionic liquids as herbicides with improved physicochemical properties and biological activity

2021 ◽  
pp. 116452
Author(s):  
Tomasz Rzemieniecki ◽  
Marta Wojcieszak ◽  
Katarzyna Materna ◽  
Tadeusz Praczyk ◽  
Juliusz Pernak
Keyword(s):  
Biological Activity ◽  
Ionic Liquids ◽  
Physicochemical Properties ◽  
Double Salt ◽  
Synthetic Auxin

4D-QSAR models applied to the study of TGF- β1 receptor inhibitors

2021 ◽  
Vol 21 ◽  
Author(s):  
Tamiris Maria de Assis ◽  
Teodorico Castro Ramalho ◽  
Elaine Fontes Ferreira da Cunha
Keyword(s):  
External Validation ◽  
Qsar Model ◽  
Dynamics Simulation ◽  
Conformational Ensemble ◽  
Pyrimidine Derivatives ◽  
Statistical Parameters ◽  
Data Set ◽  
Mesenchymal Transition ◽  
Qsar Models ◽  
Tgf Β1

Background: The quantitative structure-activity relationship is an analysis method that can be applied for designing new molecules. In 1997, Hopfinger and coworkers developed the 4D-QSAR methodology aiming to eliminate the question of which conformation to use in a QSAR study. In this work, the 4D-QSAR methodology was used to quantitatively determine the influence of structural descriptors on the activity of aryl pyrimidine derivatives as inhibitors of the TGF-β1 receptor. The members of the TGF-β subfamily are interesting molecular targets, since they play an important function in the growth and development of cell cellular including proliferation, apoptosis, differentiation, epithelial-mesenchymal transition (EMT), and migration. In late stages, TGF-β exerts tumor-promoting effects, increasing tumor invasiveness, and metastasis. Therefore, TGF-β is an attractive target for cancer therapy. Objective: The major goal of the current research is to develop 4D-QSAR models aiming to propose new structures of aryl pyrimidine derivatives. Materials and Methods: Molecular dynamics simulation was carried out to generate the conformational ensemble profile of a data set with aryl pyrimidine derivatives. The conformations were overlaid into a three-dimensional cubic box, according to the three-ordered atom alignment. The occupation of the grid cells by the interaction of pharmacophore elements provides the grid cell occupancy descriptors (GCOD), the dependent variables used to build the 4D-QSAR models. The best models were validated (internal and external validation) using several statistical parameters. Docking molecular studies were performed to better understand the binding mode of pyrimidine derivatives inside the TGF-β active site. Results : The 4D-QSAR model presented seven descriptors and acceptable statistical parameters (R2 = 0.89, q2 = 0.68, R2pred = 0.65, r2m = 0.55, R2P = 0.68 and R2rand = 0.21) besides pharmacophores groups important for the activity of these compounds. The molecular docking studies helped to understand the pharmacophoric groups and proposed substituents that increase the potency of aryl pyrimidine derivatives. Conclusion: The best QSAR model showed adequate statistical parameters that ensure their fitness, robustness, and predictivity. Structural modifications were assessed, and five new structures were proposed as candidates for a drug for cancer treatment.

scholarly journals A Computational Approach on Acetaminophen Drug using Degree-Based Topological Indices and M-Polynomials

2021 ◽  
Vol 12 (6) ◽  
pp. 7249-7266
Keyword(s):  
Biological Activity ◽  
Physicochemical Properties ◽  
Chemical Structure ◽  
Topological Index ◽  
Chemical Reactivity ◽  
Chemical Compounds ◽  
Computational Approach ◽  
Topological Indices ◽  
Numerical Representation ◽  
Essential Drug

Topological index is a numerical representation of a chemical structure. Based on these indices, physicochemical properties, thermodynamic behavior, chemical reactivity, and biological activity of chemical compounds are calculated. Acetaminophen is an essential drug to prevent/treat various types of viral fever, including malaria, flu, dengue, SARS, and even COVID-19. This paper computes the sum and multiplicative version of various topological indices such as General Zagreb, General Randić, General OGA, AG, ISI, SDD, Forgotten indices M-polynomials of Acetaminophen. To the best of our knowledge, for the Acetaminophen drugs, these indices have not been computed previously.

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