Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

scholarly journals VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR

2017 ◽  
Vol 10 (12) ◽  
pp. 150
Author(s):  
Shobana Sugumar
Keyword(s):  
Virtual Screening ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Pharmacophore Modeling ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Hydrogen Bond Acceptor ◽  
Structure Activity

  Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score, and with that, a five-point pharmacophore model was developed consisting of two hydrogen bond acceptor and three ring atoms, and the pharmacophore hypothesis yielded a statistically significant three-dimensional QSAR (3D-QSAR) model with a correlation coefficient of r2=0.8962 as well as good predictive power.Conclusion: The pharmacophore-based 3D-QSAR model generated from natural antihistamines can provide intricate structural knowledge about a new class of anti-allergic and anti-inflammatory drug research.

QSAR and Ab Initio studies of quinolon-4(1H)-imine derivatives as antimalarial agents

2021 ◽  
Vol 4 (1) ◽  
pp. 192
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo ◽  
Saprini Hamdiani
Keyword(s):  
Antimalarial Activity ◽  
Molecular Orbital Calculation ◽  
Qsar Model ◽  
Basis Set ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

Study on antimalarial activity of 22 quinolon-4(1H)-imine derivatives by using Quantitative Structure-Activity Relationships (QSAR) has been performed. Electronic and molecular descriptors were used in Quantitative Structure-Activity Relationships (QSAR) model and it was obtained from Hartree-Fock (HF) molecular orbital calculation with 6-31G basis set. QSAR analysis has been performed by multiple linear regression (MLR) method. The best equation of QSAR model on this study is: pEC50 = -4,177 + (37,902 x qC3) + (171,282 x qC8) + (9,061 x qC10) + (125,818 x qC11) + (-149,125 x qC17) + (191,623 x qC18), with statistical parameters, n = 22; r2 = 0,910; SEE = 0,171; Fcal/Ftab = 4,510 and PRESS = 0,697. The best equation can applied to design and predict new compounds with higher antimalarial activity.

scholarly journals QSAR treatment of meisoindigo derivatives as a potentbreast anticancer agent

2019 ◽  
Vol 2 (2) ◽  
pp. 114 ◽  
Author(s):  
Evie Kama Lestari ◽  
Agus Dwi Ananto ◽  
Maulida Septiyana ◽  
Saprizal Hadisaputra
Keyword(s):  
Anticancer Activity ◽  
Anticancer Agent ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Pm3 Method ◽  
Structure Activity ◽  
Semiempirical Pm3 Method

A quantitative structure-activity relationship (QSAR) analysis of meisoindigo derivatives as a breast anticancer has been carried out. This study aimed to obtain the best QSAR model in order to design new meisoindigo based compounds with best anticancer activity. The semiempirical PM3 method was used for descriptor calculation. The best QSAR model was built using multilinear regression (MLR) with enter method. It was found that there were 19 new meisoindigo derivativeswith better predictive a potent anticancer agent. The best compound was (E)-2-(1-((3-ethylisoxazol-5-yl)methyl)-2-oxoindolin-3-ylidene)-N-(4-methoxyphenyl)acetamide with the value of IC505.31144 x10-15 (μM).

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR) OF VINCADIFFORMINE ANALOGUES AS THE ANTIPLASMODIAL COMPOUNDS OF THE CHLOROQUINOSENSIBLE STRAIN

2010 ◽  
Vol 5 (3) ◽  
pp. 255-260
Author(s):  
Iqmal Tahir ◽  
Mudasir Mudasir ◽  
Irza Yulistia ◽  
Mustofa Mustofa
Keyword(s):  
Molecular Orbital ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Relationship Analysis ◽  
Structure Activity

Quantitative Structure-Activity Relationship (QSAR) analysis of vincadifformine analogs as an antimalarial drug has been conducted using atomic net charges (q), moment dipole (), LUMO (Lowest Unoccupied Molecular Orbital) and HOMO (Highest Occupied Molecular Orbital) energies, molecular mass (m) as well as surface area (A) as the predictors to their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities were taken as the activity of the drugs against chloroquine-sensitive Plasmodium falciparum (Nigerian Cell) strain and were presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best QSAR model has been determined by multiple linier regression analysis giving QSAR equation: Log (1/IC50) = 9.602.qC1 -17.012.qC2 +6.084.qC3 -19.758.qC5 -6.517.qC6 +2.746.qC7 -6.795.qN +6.59.qC8 -0.190. -0.974.ELUMO +0.515.EHOMO -0.274. +0.029.A -1.673 (n = 16; r = 0.995; SD = 0.099; F = 2.682)   Keywords: QSAR analysis, antimalaria, vincadifformine.  

scholarly journals Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis

2013 ◽  
Vol 13 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Mudasir Mudasir ◽  
Yari Mukti Wibowo ◽  
Harno Dwi Pranowo
Keyword(s):  
Sulfonic Acid ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Benzene Sulfonic Acid ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Qsar Models ◽  
New Compounds

Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.

scholarly journals Rational Design of Novel Inhibitors of α-Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening

2021 ◽  
Vol 14 (5) ◽  
pp. 482
Author(s):  
Sobia Ahsan Halim ◽  
Sumaira Jabeen ◽  
Ajmal Khan ◽  
Ahmed Al-Harrasi
Keyword(s):  
Diabetes Mellitus ◽  
Virtual Screening ◽  
Correlation Coefficient ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Inhibitory Activities

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r2 = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r2 (q2 = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r2 value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

2019 ◽  
Vol 15 (6) ◽  
pp. 588-601 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Rua'a A. Al-Aqtash ◽  
Mutasem O. Taha
Keyword(s):  
Virtual Screening ◽  
Inflammatory Diseases ◽  
Qsar Model ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Physicochemical Descriptors ◽  
Qsar Modelling ◽  
Ic50 Values ◽  
Phosphoinositide 3 Kinase ◽  
Pharmacophore Models

<P>Background: PI3K&#948; is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3K&#948; inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3K&#948; binding. </P><P> Methods: Seventeen PI3K&#948; crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). </P><P> Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. </P><P> Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3K&#948; inhibitors.</P>

The quantitative structure-activity relationships between GABAA receptor and ligands based on binding interface characteristic

2020 ◽  
Vol 16 ◽  
Author(s):  
Shu Cheng ◽  
Yanrui Ding
Keyword(s):  
Gabaa Receptor ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Receptor Interaction ◽  
Gamma Aminobutyric Acid ◽  
Quantitative Structure ◽  
Binding Interface ◽  
Structure Activity

Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Method: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03 respectively. Conclusion : We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

2020 ◽  
Vol 10 (1) ◽  
pp. 44-60
Author(s):  
Mohamed E.I. Badawy ◽  
Entsar I. Rabea ◽  
Samir A.M. Abdelgaleil
Keyword(s):  
Biological Activity ◽  
Plant Pathogens ◽  
Molecular Descriptors ◽  
Microbial Pathogens ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Structure Activity ◽  
Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

Sign in / Sign up

Export Citation Format

Share Document