scholarly journals Profiling the structural determinants of aminoketone derivatives as hNET and hDAT reuptake inhibitors by field-based QSAR based on molecular docking

2021 ◽  
Vol 29 ◽  
pp. 257-273
Author(s):  
Panpan Wang ◽  
Chenxi Jing ◽  
Pei Yu ◽  
Meng Lu ◽  
Xiaobo Xu ◽  
...  
Keyword(s):  
Neurodegenerative Disorders ◽  
Medicinal Chemistry ◽  
Structural Features ◽  
Clinical Settings ◽  
Training Set ◽  
Structural Determinants ◽  
Test Set ◽  
Contour Maps ◽  
Pharmacokinetic Properties ◽  
Derivatives Of

BACKGROUND: Bupropion, one of the dual norepinephrine and dopamine reuptake inhibitors (NDRIs), is an aminoketone derivative performed effect in improving cognitive function for depression. However, its therapeutic effect is unsatisfactory due to poor clinical response, and there are only few derivatives in pre-clinical settings. OBJECTIVE: This work attempted to elucidate the essential structural features for the activity and designed a series of novel derivatives with good inhibitive ability, pharmacokinetic and medicinal chemistry properties. METHODS: The field-based QSAR of aminoketone derivatives of two targets were established based on docking poses, and the essential structural properties for designing novel compounds were supplied by comparing contour maps. RESULTS: The selected two models performed good predictability and reliability with R2 of 0.8479 and 0.8040 for training set, Q2 of 0.7352 and 0.6266 for test set respectively, and the designed 29 novel derivatives performed no less than the highest active compound with good ADME/T pharmacokinetic properties and medicinal chemistry friendliness. CONCLUSIONS: Bulky groups in R1, bulky groups with weak hydrophobicity in R3, and potent hydrophobic substituted group with electronegative in R2 from contour maps provided important insights for assessing and designing 29 novel NDRIs, which were considered as candidates for cognitive dysfunction with depression or other related neurodegenerative disorders.

scholarly journals Structural Features of Quercetin Derivatives by Using Pharmaco-phore Modeling Approach

2016 ◽  
Vol 3 (1) ◽  
pp. 79-98
Author(s):  
Nixon Mendez ◽  
Md. Afroz Alam
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Pharmacophore Model ◽  
Cancer Cell Lines ◽  
Structural Features ◽  
Cancer Drug ◽  
Training Set ◽  
Test Set ◽  
Quercetin Derivatives

Background:Quercetin which is a natural occurring flavonoid, exert a direct pro-apoptotic effect on tumor cells by blocking the growth of several cancer cell lines at different phases of the cell cycle. Quercetin derivatives have attracted considerable attention for their cytotoxity against human cancer cell lines. In this study the derivatives of Quercetin were used for docking followed by pharmacophore modeling for studying the 3D features and configurations responsible for biological activity of structurally diverse compounds.Objective:To develop a model which depicts the crucial structural features responsible for anti-lung cancer activities.Method:A robust pharmacophore developed for the receptor have been analyzed to identify potential areas of selectivity in the hyperspace of 3D pharmacophores that may lead to the discovery of anti-lung cancer drug or such compounds which could serve as templates for the design of new molecules as potential anti lung cancer agents.Results:The generated best pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R2= 0.86 for training set and R2= 0.76 for the test set molecules. The Cross validation regression coefficient is Q2= 0.84 for training set and Q2= 0.5 for test set molecules.Conclusion:The R2and Q2reveals that pharmacophore model provide insights into the structural and chemical features of the EGFR inhibitors of Quercetin derivatives that can be used as lead compound for further synthesis as well as for screening other similar novel inhibitors of EGFR.

scholarly journals Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3271 ◽  
Author(s):  
Imane Naboulsi ◽  
Aziz Aboulmouhajir ◽  
Lamfeddal Kouisni ◽  
Faouzi Bekkaoui ◽  
Abdelaziz Yasri
Keyword(s):  
Tyrosine Kinases ◽  
Kinase Inhibitors ◽  
Structural Features ◽  
Ann Model ◽  
Training Set ◽  
Test Set ◽  
Lyn Kinase ◽  
Physico Chemical ◽  
The Mean ◽  
Artificial Neural Network Ann

Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors.

scholarly journals A Predictive HQSAR Model for a Series of Tricycle Core Containing MMP-12 Inhibitors with Dibenzofuran Ring

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jamal Shamsara ◽  
Ahmad Shahir-Sadr
Keyword(s):  
Predictive Ability ◽  
Binding Pocket ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Training Set ◽  
Color Code ◽  
Test Set ◽  
Relationship Model ◽  
Predicted Values ◽  
Good Agreement

MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q2 value of 0.697 and conventional r2 value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (rpred2=0.8733). Then, the external validity of the model was confirmed by Golbraikh-Tropsha and rm2 metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

A Comprehensive Review on the Therapeutic Versatility of Imidazo [2,1-b]thiazoles

2020 ◽  
Vol 27 (40) ◽  
pp. 6864-6887 ◽  
Author(s):  
Mohd Adil Shareef ◽  
Irfan Khan ◽  
Bathini Nagendra Babu ◽  
Ahmed Kamal
Keyword(s):  
Literature Review ◽  
Medicinal Chemistry ◽  
Therapeutic Agents ◽  
Present Review ◽  
Thiazole Derivatives ◽  
Pharmacological Activities ◽  
Comprehensive Review ◽  
Derivatives Of

Background:: Imidazo[2,1-b]thiazole, a well-known fused five-membered hetrocycle is one of the most promising and versatile moieties in the area of medicinal chemistry. Derivatives of imidazo[2,1-b]thiazole have been investigated for the development of new derivatives that exhibit diverse pharmacological activities. This fused heterocycle is also a part of a number of therapeutic agents. Objective:: To review the extensive pharmacological activities of imidazo[2,1-b]thiazole derivatives and the new molecules developed between 2000-2018 and their usefulness. Method:: Thorough literature review of all relevant papers and patents was conducted. Conclusion:: The present review, covering a number of aspects, is expected to provide useful insights in the design of imidazo[2,1-b]thiazole-based compounds and would inspire the medicinal chemists for a comprehensive and target-oriented information to achieve a major breakthrough in the development of clinically viable candidates.

Therapeutic Journey of 5-Pyrazolones as a Versatile Scaffold: A Review

2021 ◽  
Vol 21 ◽  
Author(s):  
Ravinder Sharma ◽  
Pooja A. Chawla ◽  
Viney Chawla ◽  
Rajeev Verma ◽  
Nandita Nawal ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Protease Inhibitor ◽  
Medicinal Chemistry ◽  
Membered Ring ◽  
Present Review ◽  
Biological Profile ◽  
3C Protease ◽  
Structure Activity ◽  
Derivatives Of ◽  
Heterocyclic Moiety

Abstract: A sizeable proportion of currently marketed drugs come from heterocycles. The heterocyclic moiety 5-pyrazolone is well known five membered ring containing nitrogen. Derivatives of this wonder nucleus have exhibited activities as diverse as antimicrobial, anti-inflammatory, analgesic, antidepressant, anticonvulsant, antidiabetic, antihyperlipidemic, antiviral, antitubercular, antioxidant, anticancer and antiviral including action against severe acute respiratory syndrome (SARS) or 3C protease inhibitor. A number of drugs based on this motif have already made it to the market. Standard texts and literature on medicinal chemistry cite different approaches for the synthesis of 5-pyrazolones. The present review provides an insight view to 5-pyrazolone synthesis, their biological profile and structure activity relationship studies.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

scholarly journals Feature-Weighted Sampling for Proper Evaluation of Classification Models

2021 ◽  
Vol 11 (5) ◽  
pp. 2039
Author(s):  
Hyunseok Shin ◽  
Sejong Oh
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Classification Model ◽  
Training Set ◽  
Test Set ◽  
Feature Importance ◽  
Proper Training ◽  
Machine Learning Applications ◽  
Test Sets ◽  
The Given

In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.

scholarly journals Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

2021 ◽  
Author(s):  
Rui Guo ◽  
Xiaobin Hu ◽  
Haoming Song ◽  
Pengpeng Xu ◽  
Haoping Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fdg Pet ◽  
Cell Lymphoma ◽  
Training Set ◽  
Test Set ◽  
Natural Killer T Cell ◽  
Pet Ct ◽  
Weakly Supervised ◽  
Fdg Pet Ct ◽  
Killer T Cell

Abstract Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results. Methods One hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features. Results PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods. Conclusion The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

scholarly journals Cationic Amphiphiles, a New Generation of Antimicrobials Inspired by the Natural Antimicrobial Peptide Scaffold

2010 ◽  
Vol 54 (10) ◽  
pp. 4049-4058 ◽  
Author(s):  
Brandon Findlay ◽  
George G. Zhanel ◽  
Frank Schweizer
Keyword(s):  
Cytoplasmic Membrane ◽  
Resistant Bacteria ◽  
Clinical Settings ◽  
Specific Amino Acid ◽  
Cationic Amphiphiles ◽  
Naturally Occurring ◽  
Pharmacokinetic Properties ◽  
To Come ◽  
New Generation

ABSTRACT Naturally occurring cationic antimicrobial peptides (AMPs) and their mimics form a diverse class of antibacterial agents currently validated in preclinical and clinical settings for the treatment of infections caused by antimicrobial-resistant bacteria. Numerous studies with linear, cyclic, and diastereomeric AMPs have strongly supported the hypothesis that their physicochemical properties, rather than any specific amino acid sequence, are responsible for their microbiological activities. It is generally believed that the amphiphilic topology is essential for insertion into and disruption of the cytoplasmic membrane. In particular, the ability to rapidly kill bacteria and the relative difficulty with which bacteria develop resistance make AMPs and their mimics attractive targets for drug development. However, the therapeutic use of naturally occurring AMPs is hampered by the high manufacturing costs, poor pharmacokinetic properties, and low bacteriological efficacy in animal models. In order to overcome these problems, a variety of novel and structurally diverse cationic amphiphiles that mimic the amphiphilic topology of AMPs have recently appeared. Many of these compounds exhibit superior pharmacokinetic properties and reduced in vitro toxicity while retaining potent antibacterial activity against resistant and nonresistant bacteria. In summary, cationic amphiphiles promise to provide a new and rich source of diverse antibacterial lead structures in the years to come.

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