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>

scholarly journals Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1107 ◽  
Author(s):  
Ji-Xia Ren ◽  
Rui-Tao Zhang ◽  
Hui Zhang
Keyword(s):  
Virtual Screening ◽  
Correlation Coefficient ◽  
3D Qsar ◽  
Docking Study ◽  
Qsar Model ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Qsar Modelling ◽  
Pharmacophore Models ◽  
Test Database

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

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.

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.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Pharmacophore Modelling and Virtual Screening Studies for the Discovery of Potential Natural Products Based PDE1B Inhibitor Lead Compounds

2021 ◽  
Vol 22 ◽  
Author(s):  
Teng Woei Shy ◽  
Anand Gaurav
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Natural Product ◽  
Neuropsychiatric Disorders ◽  
Major Health ◽  
Pharmacophore Modelling ◽  
Lead Compounds ◽  
Medical Need ◽  
Total Brain ◽  
Pharmacophore Models

Aim: The aim of the present study was to apply pharmacophore based virtual screening to a natural product database to identify potential PDE1B inhibitor lead compounds for neurodegenerative and neuropsychiatric disorders. Background: Neurodegenerative and neuropsychiatric disorders are a major health burden globally. The existing therapies do not provide optimal relief and are associated with substantial adverse effects. This has resulted in a huge unmet medical need for newer and more effective therapies for these disorders. Phosphodiesterase (PDEs) enzymes have been identified as potential targets of drugs for neurodegenerative and neuropsychiatric disorders, and one of the subtypes, i.e., PDE1B, accounts for more than 90 % of total brain PDE activity associated with learning and memory process, making it an interesting drug target for the treatment of neurodegenerative disorders. Objectives: The present study has been conducted to identify potential PDE1B inhibitor lead compounds from the natural product database. Methods: Ligand-based pharmacophore models were generated and validated; they were then employed for virtual screening of Universal Natural Products Database (UNPD) followed by docking with PDE1B to identify the best hit compound. Results: Virtual screening led to the identification of 85 compounds which were then docked into the active site of PDE1B. Out of the 85 compounds, six showed a higher affinity for PDE1B than the standard PDE1B inhibitors. The top scoring compound was identified as Cedreprenone. Conclusion: Virtual screening of UNPD using Ligand based pharmacophore led to the identification of Cedreprenone, a potential new natural PDE1B inhibitor lead compound.

Virtual Screening Studies for Discovery of Novel Inhibitors of Inflammatory Process Targets

2018 ◽  
Vol 24 (14) ◽  
pp. 1617-1638 ◽  
Author(s):  
Marcus Tullius Scotti ◽  
Mateus Feitosa Alves ◽  
Chonny Alexander Herrera-Acevedo ◽  
Luciana Scotti
Keyword(s):  
Virtual Screening ◽  
Infectious Diseases ◽  
Inflammatory Diseases ◽  
Computational Cost ◽  
Inflammatory Conditions ◽  
Inflammatory Drugs ◽  
The Stability ◽  
Dynamics Simulations ◽  
Potential Inhibitors ◽  
Pharmacophore Models

Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases.

Structural Investigations of Aroylindole Derivatives through 3D-QSAR and Multiple Pharmacophore Modeling for the Search of Novel Colchicines Inhibitor

2020 ◽  
Vol 17 ◽  
Author(s):  
Vijay K. Patel ◽  
Harish Rajak
Keyword(s):  
Virtual Screening ◽  
Anticancer Agents ◽  
Structural Alignment ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Structural Features ◽  
Pharmacophore Modeling ◽  
Structural Investigations ◽  
Pharmacophore Modelling

Background : The ligand and structure based integrated strategies are being repeatedly and effectively employed for the precise search and design of novel ligands against various disease targets. Aroylindole derivative have a similar structural analogy as Combretastatin A-4, and exhibited potent anticancer activity on several cancer cell lines. Objective: To identify structural features of aroylindole derivatives through 3D-QSAR and multiple pharmacophore modelling for the search of novel colchicines inhibitor via virtual screening. Method: The present study utilizes ligand and structure based methodology for the establishment of structure activity correlation among trimethoxyaroylindole derivatives and search of novel colchicines inhibitor via virtual screening. The 3DQSAR studies were performed using Phase module and provided details of relationship between structure and biological activity. A single ligand based pharmacophore model was generated from Phase on compound 3 and compound 29 and three energetically optimized structure based pharmacophore models were generated from e-pharmacophore for co-crystallized ligand, compound 3 and compound 29 with protein PBD ID 1SA0, 5EYP and 5LYJ. These pharmacophoric features containing hit-like compounds were collected from commercially available ZINC database and screened using virtual screening workflow. Results and Discussion: The 3D-QSAR model studies with good PLSs statistics for factor four was characterized by the best prediction coefficient Q2 (0.8122), regression R2 (0.9405), SD (0.2581), F (102.7), P (1.56e-015), RMSE (0.402), Stability (0.5411) and Pearson-r (0.9397). The generated e-pharmacophores have GH scores over 0.5 and AUAC ≥ 0.7 indicated that all the pharmacophores were suitable for pharmacophore-based virtual screening. The virtual screened compounds ZINC12323179, ZINC01642724, ZINC14238006 have showed similar structural alignment as co-crystallized ligand and showed the hydrogen bonding of ligand with ASN101, SER178, THR179, VAL238, CYS241 amino acid of protein. Conclusion: The study illustrates that the ligand and structure based pharmacophoric approach is beneficial for identification of structurally diverse hits, having better binding affinity on colchicines binding site as novel anticancer agents.

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.

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.

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