scholarly journals AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

2021 ◽  
Vol 22 (8) ◽  
pp. 3944
Author(s):  
Amit Kumar Halder ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Linear Model ◽  
Inhibitory Activity ◽  
Kinase Inhibitor ◽  
External Validation ◽  
Systematic Evaluation ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Linear Discriminant ◽  
Non Linear ◽  
Qsar Models

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

scholarly journals Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3909 ◽  
Author(s):  
Amit Kumar Halder ◽  
Amal Kanta Giri ◽  
Maria Natália Dias Soeiro Cordeiro
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Linear Model ◽  
Anticancer Agents ◽  
Quantitative Structure Activity Relationship ◽  
Experimental Conditions ◽  
Fragment Analysis ◽  
Linear Discriminant ◽  
Qsar Models ◽  
Dynamics Simulations

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

scholarly journals Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

2008 ◽  
Vol 13 (10) ◽  
pp. 1014-1024 ◽  
Author(s):  
Gerardo M. Casañola-Martín ◽  
Yovani Marrero-Ponce ◽  
Mahmud Tareq Hassan Khan ◽  
Francisco Torrens ◽  
Facundo Pérez-Giménez ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Inhibitory Activity ◽  
External Validation ◽  
Qsar Study ◽  
Good Correspondence ◽  
Linear Discriminant ◽  
Tyrosinase Inhibitory Activity ◽  
Starting Point ◽  
Qsar Models ◽  
Antityrosinase Activity

Two-dimensional atom- and bond-based TOMOCOMD-CARDD descriptors and linear discriminant analysis (LDA) are used in this report to perform a quantitative structure-activity relationship (QSAR) study of tyrosinase-inhibitory activity. A database of inhibitors of the enzyme is collected for this study, within 246 highly dissimilar molecules presenting antityrosinase activity. In total, 7 discriminant functions are obtained by using the whole set of atom- and bond-based 2D indices. All the LDA-based QSAR models show accuracies above 90% in the training set and values of the Matthews correlation coefficient ( C) varying from 0.85 to 0.90. The external validation set shows globally good classifications between 89% and 91% and C values ranging from 0.75 to 0.81. Finally, QSAR models are used in the selection/identification of the 20 new dicoumarins subset to search for tyrosinase inhibitory activity. Theoretical and experimental results show good correspondence between one another. It is important to remark that most compounds in this series exhibit a more potent inhibitory activity against the mushroom tyrosinase enzyme than the reference compound, Kojic acid (IC50 = 16.67 μM), resulting in a novel nucleus base (lead) with antityrosinase activity, and this could serve as a starting point for the drug discovery of novel tyrosinase inhibitor lead compounds. ( Journal of Biomolecular Screening 2008:1014-1024)

Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives

2019 ◽  
Vol 15 (3) ◽  
pp. 212-224
Author(s):  
Paria Ghaemian ◽  
Ali Shayanfar
Keyword(s):  
Image Analysis ◽  
Correlation Coefficient ◽  
Inhibitory Activity ◽  
Linear Models ◽  
External Validation ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Test Set ◽  
Non Linear ◽  
Qsar Models

<P>Background: Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance. Objective: In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives. Methods: The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation. Results: Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models. Conclusion: According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.</P>

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

2013 ◽  
pp. 322-427
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Classification Models ◽  
Training Set ◽  
Linear Discriminant ◽  
Oecd Principles ◽  
Qsar Models ◽  
Validation Set ◽  
Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

2020 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Shinjita Ghosh ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Anas Platyrhynchos ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Avian Species ◽  
Industrial Chemicals ◽  
Linear Discriminant ◽  
Topological Distance ◽  
Qsar Models ◽  
Classification And Regression

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

2012 ◽  
Vol 2 (2) ◽  
pp. 42-144
Author(s):  
Gerardo M. Casañola-Martín ◽  
Mahmud Tareq Hassan Khan ◽  
Huong Le-Thi-Thu ◽  
Yovani Marrero-Ponce ◽  
Ramón García-Domenech ◽  
...  
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Training Set ◽  
Atom Type ◽  
Linear Discriminant ◽  
Oecd Principles ◽  
Qsar Models ◽  
Validation Set ◽  
Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

scholarly journals Development of Generalized QSAR Models for Predicting Cytotoxicity and Genotoxicity of Metal Oxides Nanoparticles

2020 ◽  
Vol 5 (4) ◽  
pp. 83-100
Author(s):  
Pravin Ambure ◽  
Arantxa Ballesteros ◽  
Francisco Huertas ◽  
Pau Camilleri ◽  
Stephen J. Barigye ◽  
...  
Keyword(s):  
Food Packaging ◽  
Moving Average ◽  
Metal Oxide Nanoparticles ◽  
Structural Features ◽  
Industrial Applications ◽  
Consumer Products ◽  
Machine Learning Techniques ◽  
Experimental Conditions ◽  
Linear Discriminant ◽  
Qsar Models

In recent years, nanomaterials have gained tremendous attention due to their wide variety of industrial applications including food packaging, consumer products, nanomedicines, etc. The fascinating properties of nanoparticles which are responsible for creating several exciting opportunities, however, are also accountable for growing concerns of their toxic effects on humans as well as the environment. Thus, in the present study, the authors have developed generalized models for predicting the cytotoxicity and genotoxicity of seven metal oxide nanoparticles. The models not only take into account the structural features, but also the diverse experimental conditions under which the toxicity of nanoparticles was determined. The diverse experimental conditions were captured in the generalized models using the Box-Jenkins moving average approach. Here, two machine learning techniques, namely, linear discriminant analysis and random forest were utilized to build the final models. Importantly, the validation metrics showed that the developed models have significant discriminatory power.

scholarly journals On the Estimation of Permeabilities and Draw Resistances of Cigarette Components

2004 ◽  
Vol 21 (1) ◽  
pp. 25-31
Author(s):  
B Eitzinger
Keyword(s):  
Flow Velocity ◽  
Linear Model ◽  
Unique Solution ◽  
Condition Number ◽  
Noise Levels ◽  
Estimation Task ◽  
Experimental Conditions ◽  
Non Linear ◽  
High Bias ◽  
Cigarette Paper

AbstractThe goal of this study is to investigate whether the permeability of the tipping/plugwrap system, the permeability of the cigarette paper and the draw resistances of the filter and tobacco rod can be calculated from measurements of the degree of filter ventilation and of the open and closed draw resistance. This issue is investigated for a linear and a non-linear model of the flow in unlit cigarettes. At first it is proven that there exist experimental conditions to which the cigarette can be exposed such that the problem has at least a unique solution. The problem is then solved by least-squares optimisation for a linear and a non-linear model of the air flow in unlit cigarettes with various noise levels on the output quantities. The error sensitivity of the optimisation problem is estimated by calculation of the condition number.From the simulation several facts can be concluded. Firstly, for the linear model varying the flow velocity at the mouth end of the cigarette does not provide enough information to uniquely determine the properties of the cigarette's components. Secondly, estimates of these properties from the linear model have low standard deviations but a high bias, which makes the linear model useless for the estimation task. Thirdly, estimates from the non-linear model are more reliable if the pressure at the cigarette tip is varied instead of the flow velocity at the mouth end. Fourthly, the measurements of the degree of filter ventilation and of the open and closed draw resistance need to be at least 10 to 20 times more accurate than the desired accuracy of the estimate. Several methods to improve this situation are proposed.

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