scholarly journals Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Anubhuti Pandey ◽  
Sarvesh Kumar Paliwal ◽  
Shailendra Kumar Paliwal
Keyword(s):  
Predictive Power ◽  
Pharmacophore Model ◽  
Urotensin Ii ◽  
Hydrogen Bond Acceptor ◽  
Test Set ◽  
Lead Compounds ◽  
Mapping Analysis ◽  
Feature Based ◽  
The Cost ◽  
Early Drug

For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.

3D QSAR Studies on Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents

2009 ◽  
Vol 2 (2) ◽  
pp. 557-565
Author(s):  
Shaheen Begum ◽  
Satya Parameshwar K ◽  
Ravindra G K ◽  
Achaiah G
Keyword(s):  
Hydrogen Bond ◽  
Antifungal Activity ◽  
Drug Research ◽  
3D Qsar ◽  
Qsar Model ◽  
Hydrogen Bond Acceptor ◽  
Test Set ◽  
Qsar Studies ◽  
The Cost ◽  
Anti Fungal

Benzoxazoles and Oxazolo-[4,5-b]pyridines  have been reported as potent anti-fungal agents. 3D QSAR tools including CoMFA and CoMSIA have been known to be a promising approaches is to correlate structures and activity which further enable the medicinal chemists to design more potent molecules thus curtailing the cost and time in drug research. CoMFA and CoMSIA studies have been carried out on 31 molecules of benzoxazole and oxazolopyridines in order to determine the structural properties required for effective antifungal activity. 26 compounds were evaluated for establishing QSAR model, which was then validated by predicting the activities of five test set molecules. All the molecules were aligned by SYBYL database alignment which led to a best model with q2 value of 0.835, r2=0.976 and r2pred=0.773. This model was further employed to derive CoMSIA models, a best model with steric, electrostatic, hydrophobic and hydrogen bond acceptor indices exhibited q2 = 0.812, r2=0.971 and r2pred=0.81. The models thus obtained from this study can be useful for the design and development of new potential anti-fungal agents.

scholarly journals Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3204 ◽  
Author(s):  
Yoon Lee ◽  
Gwan-Su Yi
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Correlation Coefficient ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Test Set

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

scholarly journals DESIGN AND SYNTHESIS OF SOME NEWER IMIDAZOLYL HETEROCYCLES AS POTENT BTK INHIBITORS FOR THE TREATMENT OF RHEUMATOID ARTHRITIS

2017 ◽  
Vol 9 (3) ◽  
pp. 80
Author(s):  
R. Priyadarsini ◽  
Anandhan Menaka
Keyword(s):  
Rheumatoid Arthritis ◽  
Hydrogen Bond ◽  
Molecular Docking ◽  
Tyrosine Kinase ◽  
Pharmacophore Model ◽  
Spectral Studies ◽  
Bruton’S Tyrosine Kinase ◽  
Bruton's Tyrosine Kinase ◽  
Hydrogen Bond Acceptor ◽  
Btk Inhibitors

Objective: The rheumatoid arthritis as a global health problem over the past few decades, Emphasizes the need for discovery of new therapeutic disease modifying anti-rheumatoid Arthritis drugs (DMARD’s). Bruton’s tyrosine kinase (BTK) is a cytoplasmic, non-receptor, tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in the development, differentiation and proliferation of B-lineage cells, thus making BTK an efficient therapeutic target for the treatment of rheumatoid arthritis. This prompted us to synthesise a novel series of Imidazolyl Heterocycles as potent BTK (Bruton’s Tyrosine Kinase) inhibitors with alleged Anti-Rheumatoid Arthritis properties. Methods: Newer BTK inhibitors containing one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) and three hydrophobic features based on that pharmacophore model for BTK were designed. The designed compounds were sorted by applying ADMET properties, Lipinski rule of five, molecular docking and Novelty prediction to refine the designed ligands. Finally, different five compounds containing Imidazole as the heterocyclic nucleus have been synthesized and characterized by different analytical methods like Chromatographic data, Elemental analysis and Spectral studies by IR, 1H NMR, 13C NMR, GC-MS. Molecular docking studies were performed against BTK using GLIDE 10.2. Results: Several important hydrogen bonds with BTK were revealed, which include the gatekeeper residue Glu475 and Met477 at the hinge region. Conclusion: Overall, this study suggests that the proposed ligands are found to be more effective BTK inhibitor as Anti-Rheumatoid arthritis agents.

scholarly journals Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin

2021 ◽  
Vol 28 ◽  
pp. 135-139
Author(s):  
O. V. Rayevsky ◽  
O. M. Demchyk ◽  
P. A. Karpov ◽  
S. P. Ozheredov ◽  
S. I. Spivak ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Interaction ◽  
Specific Binding ◽  
Pharmacophore Model ◽  
Computational Prediction ◽  
Mitotic Apparatus ◽  
Ligand Interaction ◽  
Lead Compounds ◽  
Key Functional Groups ◽  
Potential Inhibitors

Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual screening) were developed. The generalized pharmacophore model of ligand-protein interaction and key functional groups of ligands responsible for specific binding were identified. Conclusions. Based on results of virtual screening, 22 commercial compounds were selected. Identified compounds proposed as potential inhibitors of Plasmodium mitotic machinery and the base of new antimalarial drugs. Keywords: malaria, Plasmodium, intermolecular interaction, dinitroaniline derived, phosphorothioamidate derived.

scholarly journals Artificial Neural Networks in Drug Design 11: Influence of Learning Rate and Momentum Factor on the Predictive Ability

2000 ◽  
Vol 68 (1) ◽  
pp. 57-64 ◽  
Author(s):  
D. Kaiser ◽  
C. Tmej ◽  
P. Chiba ◽  
K.-J. Schaper ◽  
G. Ecker
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Power ◽  
Predictive Ability ◽  
Learning Rate ◽  
Data Set ◽  
Lead Compounds ◽  
Learning Rates ◽  
Artificial Neural ◽  
Nanomolar Range

A data set of 48 propafenone-type modulators of multidrug resistance was used to investigate the influence of learning rate and momentum factor on the predictive power of artificial neural networks of different architecture. Generally, small learning rates and medium sized momentum factors are preferred. Some of the networks showed higher cross validated Q2 values than the corresponding linear model (0.87 vs. 0.83). Screening of a 158 compound virtual library identified several new lead compounds with activities in the nanomolar range.

Feature-Based Test Focus Selection Technique Using Classes Connections Weight

2016 ◽  
Vol 7 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Iyad Alazzam ◽  
Mohammed Akour ◽  
Shadi Banitaan ◽  
Feras Hanandeh
Keyword(s):  
Time Constraints ◽  
Integration Testing ◽  
Development Cost ◽  
Static And Dynamic Analysis ◽  
Source Tracing ◽  
System User ◽  
Testing Cost ◽  
Feature Based ◽  
Good Mutation ◽  
The Cost

Testing could cost more than fifty percent of all development cost, particularly integration testing consumes around eighty percent of testing cost. Integration testing aims to discover errors in the connections among classes which are collaborate and communicate in order to provide specific services. Though, testing all connections among classes is impractical because of the cost, effort and time constraints. Test focus selection might help testers to concentrate on the main and vital connections among classes which it could be the most error prone ones. The authors proposed approach amalgamates the static and dynamic analysis in order to detect, trace, and weight the connections among classes through method level communications. Their approach harnessed an open source tracing tool (MUTT). The MUTT allows them to return all the methods in all classes that have been called respecting to any specific feature which has triggered by the system user. The experimental results reveal how the proposed approach achieves good mutation testing score on the systems under study.

scholarly journals The Characteristics of PD-L1 Inhibitors, from Peptides to Small Molecules

Molecules ◽  
2019 ◽  
Vol 24 (10) ◽  
pp. 1940 ◽  
Author(s):  
Yanwen Zhong ◽  
Xuanyi Li ◽  
Hequan Yao ◽  
Kejiang Lin
Keyword(s):  
Hydrogen Bond ◽  
Small Molecules ◽  
Pharmacophore Model ◽  
Amino Acid Residues ◽  
Tumor Treatment ◽  
Hydrogen Bond Donor ◽  
Treatment Results ◽  
Hydrogen Bond Acceptor ◽  
Checkpoint Pathway ◽  
Pharmacophore Models

The programmed cell death ligand protein 1 (PD-L1) is a member of the B7 protein family and consists of 290 amino acid residues. The blockade of the PD-1/PD-L1 immune checkpoint pathway is effective in tumor treatment. Results: Two pharmacophore models were generated based on peptides and small molecules. Hypo 1A consists of one hydrogen bond donor, one hydrogen bond acceptor, two hydrophobic points and one aromatic ring point. Hypo 1B consists of one hydrogen bond donor, three hydrophobic points and one positive ionizable point. Conclusions: The pharmacophore model consisting of a hydrogen bond donor, hydrophobic points and a positive ionizable point may be helpful for designing small-molecule inhibitors targeting PD-L1.

scholarly journals Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 47
Author(s):  
Arash Mirhashemi
Keyword(s):  
Dynamic Range ◽  
Specular Reflection ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Image Database ◽  
High Dynamic Range ◽  
Registration Method ◽  
Spatial Registration ◽  
Feature Based ◽  
The Cost

At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models.

Validation of a Modified Comorbidity Index for Allogeneic Hematopoietic Cell Transplant

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1167-1167 ◽  
Author(s):  
Todd E. DeFor ◽  
Chap Le ◽  
Angela R Smith ◽  
Erica D. Warlick ◽  
Nelli Bejanyan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Predictive Power ◽  
Disease Risk ◽  
Risk Index ◽  
Cell Transplant ◽  
Training Set ◽  
Hematopoietic Cell Transplant ◽  
Test Set ◽  
Comorbidity Index

Abstract INTRODUCTION Among adult allogeneic hematopoietic cell transplant (HCT) recipients, the HCT-specific comorbidity index (HCT-CI) is a standard measure of baseline comorbidity. This measure incorporates 17 different comorbidities into a combined, categorically weighted score of standard, intermediate and high risk. Using the specific weights for each comorbidity from the single center analysis, the HCT-CI has been validated in other studies, most notably in a recent analysis including 8115 HCT recipients from the United States. The HCT-CI has been useful in controlling for confounding of comorbidities among patients. We previously reported that the efficiency and predictive power could be improved by removing the conversion of adjusted hazard ratios (HR) for non-relapse mortality (NRM) to three possible weights (1-3) for each comorbidity. METHODS Because some comorbidities show effects on a continuous scale and others show no effect, we proposed a weighting scheme in which each comorbidity is assigned the natural weight based on Fine and Gray regression analysis on NRM. The final modified comorbidity index (MCI) is based on a multiplicative model controlling for age, disease risk index, donor type and stratified by conditioning intensity. In this current study, we tested validation of calculations for the MCI by randomizing 2/3 of 1114 adult allogeneic patients with prospectively collected (2000-2015) comorbidities to a training set and 1/3 of patients to a test set. Using weights from the training set, we compared the MCI to the HCT-CI for the endpoints of NRM and overall survival (OS) in the test set. We did this using regression analysis and bootstrapping the difference in C-statistics for each method. RESULTS The median patient age was 51 (IQR: 39-59), 59% were male, donors included 41% HLA-matched sibling donors, 7% matched unrelated donors (URD) and 52% umbilical cord blood (UCB). Patients had malignant diagnoses with a disease risk index (DRI) of 19% low, 62% intermediate and 19% high or very high. Conditioning intensity included 65% reduced intensity (RIC) regimens. Using the HCT-CI, 19% were classified as low, 31% as intermediate and 39% as high risk. Based on the MCI, 34% were classified as low, 54% as intermediate and 12% as high risk. After adjusting for other factors, the independent weights for each comorbidity were calculated in our training set. We calculated the MCI by exponentiating the sum of all parameter coefficients from the regression analysis. The revised index score is: MCI = exponent [0.40*(binary indicator for cardiac disorders) + 0.85*(heart valve disease) + 0.05*(inflammatory bowel disease) + 0.48*(peptic ulcer) + 0.46* (diabetes) + 0.03*(psychiatric disturbance) + 0.20*(mild hepatic function) + 0.93*(moderate/severe hepatic function) + 0.19*(infection) + 2.00*(renal insufficiency) + 0.17*(moderate pulmonary abnormalities) + 0.39*(severe pulmonary abnormalities) + 0.16*(prior solid tumor)]. Comorbidities including obesity, cerebrovascular disease and rheumatologic disorders had no influence on NRM. This on-line calculator facilitates scoring of the modified index--MCI: http://bmt.ahc.umn.edu:8082/hct. In the test set (N=372), MCI was more predictive of NRM (table, fig 1a and 1b) and showed a trend toward increased sensitivity for OS compared to the original HCT-CI. The HR for intermediate and high risk categories increased (≥60% for NRM and >30% for OS). The adjusted likelihood ratio (showing model fit) increased from 20.3 to 22.5 for NRM and from 38.9 to 40.7 for OS when substituting MCI for HCT-CI. An increase shows better prediction of the endpoint. The C-statistic reflecting more NRM with a higher score and worse survival increased from 0.540 to 0.562 for NRM (P=0.02) and increased from 0.567 to 0.594 for OS (P=0.08). DISCUSSION This new MCI showed higher discriminating and predictive power for post-HCT NRM and a trend towards more predictive power for OS. As many HCT recipients have pre-existing comorbidities, the greater discrimination in assigning patient comorbidity will better inform decision-making for HCT recipients and HCT studies by better adjustment of these important risk factors. This MCI methodology should be used to create more efficient and predictive assessments in a larger multi-center study. Disclosures No relevant conflicts of interest to declare.

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