Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients

2021 ◽  
pp. 100207
Author(s):  
Sebla Oztan Akturk ◽  
Gulcin Tugcu ◽  
Hande Sipahi
Keyword(s):  
Qsar Model ◽  
Cosmetic Ingredients

Hybrid Imidazole-Pyridine Derivatives: An Approach to Novel Anticancer DNA Intercalators

2020 ◽  
Vol 27 (1) ◽  
pp. 154-169 ◽  
Author(s):  
Claudiu N. Lungu ◽  
Bogdan Ionel Bratanovici ◽  
Maria Mirabela Grigore ◽  
Vasilichia Antoci ◽  
Ionel I. Mangalagiu
Keyword(s):  
Experimental Data ◽  
Antimicrobial Properties ◽  
Qsar Model ◽  
Therapeutic Effectiveness ◽  
Computational Results ◽  
Pyridine Derivatives ◽  
Quadruplex Dna ◽  
Dna Intercalators ◽  
Structure Activity ◽  
G Quadruplex

Lack of specificity and subsequent therapeutic effectiveness of antimicrobial and antitumoral drugs is a common difficulty in therapy. The aim of this study is to investigate, both by experimental and computational methods, the antitumoral and antimicrobial properties of a series of synthesized imidazole-pyridine derivatives. Interaction with three targets was discussed: Dickerson-Drew dodecamer (PDB id 2ADU), G-quadruplex DNA string (PDB id 2F8U) and DNA strain in complex with dioxygenase (PDB id 3S5A). Docking energies were computed and represented graphically. On them, a QSAR model was developed in order to further investigate the structure-activity relationship. Results showed that synthesized compounds have antitumoral and antimicrobial properties. Computational results agreed with the experimental data.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

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.

Immunotoxicity, Flow Cytometry, and Chemoinformatics: Review, Bibliometric Analysis, and New QSAR Model of Drug Effects Over Macrophages

2012 ◽  
Vol 12 (16) ◽  
pp. 1815-1833 ◽  
Author(s):  
Esvieta Tenorio-Borroto ◽  
Claudia G. Penuelas-Rivas ◽  
Juan C. Vasquez-Chagoyan ◽  
Francisco J. Prado-Pradoa ◽  
Xerardo Garcia-Mera ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Bibliometric Analysis ◽  
Drug Effects ◽  
Qsar Model

Identification of Main Protease of Coronavirus SARS-CoV-2 (Mpro)Inhibitors from Melissa officinalis

2020 ◽  
Vol 17 ◽  
Author(s):  
Olusola O. Elekofehinti ◽  
Opeyemi Iwaloye ◽  
Courage D. Famusiwa ◽  
Olanrewaju Akinseye ◽  
Joao B. T. Rocha
Keyword(s):  
Qsar Model ◽  
Computational Approach ◽  
Melissa Officinalis ◽  
Lead Compounds ◽  
Main Protease ◽  
The World ◽  
Recent Outbreak ◽  
Global Concern ◽  
Catalytic Dyad ◽  
Potential Therapeutic Intervention

Background: he recent outbreak of Coronavirus SARS-CoV-2 (Covid-19) which has rapidly spread around the world in about three months with tens of thousands of deaths recorded so far is a global concern. An urgent need for potential therapeutic intervention is of necessity. Mpro is an attractive druggable target for the development of anti-COVID-19 drug development. Compounds previously characterized from Melissa officinalis were queried against main protease of coronavirus SARS-CoV-2 using computational approach. Results: Melitric acid A and salvanolic acid A had higher affinity than lopinavir and ivermectin using both AutodockVina and XP docking algorithms. The computational approach was employed in the generation of QSAR model using automated QSAR, and in the docking of ligands from Melissa officinalis with SARS-CoV-2 Mpro inhibitors. The best model obtained was KPLS_Radial_28 (R2 = 0.8548 and Q2=0.6474, and was used in predicting the bioactivity of the lead compounds. Molecular mechanics based MM-GBSA confirmed salvanolic acid A as the compound with the highest free energy and predicted bioactivity of 4.777; it interacted with His-41 of the catalytic dyad (Cys145-His41) of SARS-CoV-2 main protease (Mpro), as this may hinder the cutting of inactive viral protein into active ones capable of replication. Conclusion: Salvanolic acid A can be further evaluated as potential Mpro inhibitor.

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>

Generation of Pharmacophore and Atom Based 3D-QSAR Model of Novel Isoquinolin-1-one and Quinazolin-4-one-type Inhibitors of TNFα

2012 ◽  
Vol 8 (3) ◽  
pp. 436-451 ◽  
Author(s):  
Pradeep Hanumanthappa ◽  
Mahesh K. Teli ◽  
Rajanikant G. Krishnamurthy
Keyword(s):  
3D Qsar ◽  
Qsar Model

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

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