Linear and nonlinear QSAR models of acute intravenous toxicity to mice for organic chemicals

QSAR analysis of acute intravenous toxicity to mice for 68 monofunctional chemicals is presented. There compounds represents seven classes of organic chemicals: hydrocarbons (6 chemicals), alcohols (13), amides (22), amines (12), ethers (5), ketones (7), nitriles (3). Preliminary consideration of data for these chemicals showed that it is necessary to consider not only linear toxicity - descriptors relationships, but also nonlinear models. The linear and nonlinear QSAR models were considered for each from indicated classes of organic chemicals. Analogical models were constructed for whole subset of monofunctional chemicals. The statistical parameters and robustness of nonlinear models are essential better then statistics of linear models. Replacing a lipophilicity descriptor with molecular polarizability and H-bond ability in nonlinear models permits also to improve statistical characteristics. Clearly, if relationships between the intravenous toxicity of compounds bearing only a single functional group and lipophilicity are nonlinear, then similar relationships must be considered with compounds containing more than one functional group. To check up this idea whole set of small clusters containing structure relative compounds with few functional groups was examined from position of linear and nonlinear relationships between toxicity and lipophilicity. It was estimated in most causes advantages of nonlinear models.

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QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

10.21203/rs.3.rs-125264/v1 ◽

2020 ◽

Author(s):

M. Natália Dias Soeiro Cordeiro ◽

Amit Kumar Halder

Keyword(s):

Open Source ◽

Linear Models ◽

Moving Average ◽

Biological Data ◽

Statistical Parameters ◽

Computational Tools ◽

Qsar Modelling ◽

Qsar Models ◽

Quantitative Structure Activity Relationships ◽

Set Up

Abstract Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling tools is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multi-target QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python−based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters along with graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, three case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

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Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

10.26434/chemrxiv.12196683.v1 ◽

2020 ◽

Author(s):

Vijay Masand ◽

Ajaykumar Gandhi ◽

Vesna Rastija ◽

Meghshyam K. Patil

Keyword(s):

Quantitative Structure Activity Relationship ◽

Structural Features ◽

Quantitative Structure ◽

Statistical Parameters ◽

Qsar Analysis ◽

Structure Activity ◽

Atom Pairs ◽

Main Protease ◽

Qsar Models ◽

Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div> </div>

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Information criteria for nonlinear time series models

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2015-0026 ◽

2016 ◽

Vol 20 (3) ◽

Author(s):

Saskia Rinke ◽

Philipp Sibbertsen

Keyword(s):

Error Term ◽

Linear Models ◽

Nonlinear Models ◽

General Information ◽

Information Criteria ◽

Order Selection ◽

Simulation Studies ◽

Variance Estimator ◽

Linear And Nonlinear ◽

The Impact

AbstractIn this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.

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Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents

Acta periodica technologica ◽

10.2298/apt1748117k ◽

2017 ◽

pp. 117-126

Author(s):

Milica Karadzic ◽

Strahinja Kovacevic ◽

Lidija Jevric ◽

Sanja Podunavac-Kuzmanovic

Keyword(s):

Antifungal Activity ◽

Antifungal Agents ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Linear Modeling ◽

Statistical Parameters ◽

Qsar Analysis ◽

Qsar Models

Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins.

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Forecasting Brazilian output and its turning points in the presence of breaks: a comparison of linear and nonlinear models

Estudos Econômicos (São Paulo) ◽

10.1590/s0101-41612006000100001 ◽

2006 ◽

Vol 36 (1) ◽

pp. 5-46 ◽

Cited By ~ 2

Author(s):

Brisne J. V. Céspedes ◽

Marcelle Chauvet ◽

Elcyon C. R. Lima

Keyword(s):

Business Cycle ◽

Structural Breaks ◽

Linear Models ◽

Nonlinear Models ◽

Markov Switching Models ◽

Real Gdp ◽

Switching Models ◽

Forecasting Performance ◽

Out Of Sample ◽

Linear And Nonlinear

This paper compares the forecasting performance of linear and nonlinear models under the presence of structural breaks for the Brazilian real GDP growth. The Markov switching models proposed by Hamilton (1989) and its generalized version by Lam (1990) are applied to quarterly GDP from 1975:1 to 2000:2 allowing for breaks at the Collor Plans. The probabilities of recessions are used to analyze the Brazilian business cycle. The in-sample and out-of-sample forecasting ability of growth rates of GDP of each model is compared with linear specifications and with a non-parametric rule. We find that the nonlinear models display a better forecasting performance than linear models. The specifications with the presence of structural breaks are important in obtaining a representation of the Brazilian business cycle and their inclusion improves considerably the models forecasting performance within and out-of-sample.

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Studies on the IC50 of Metabolically Stable 1-(3,3-diphenylpropyl)- piperidinyl Amides and Ureas as Human CCR5 Receptor Antagonists Based on QSAR

Letters in Drug Design & Discovery ◽

10.2174/1570180817666200320105725 ◽

2020 ◽

Vol 17 (8) ◽

pp. 1036-1046

Author(s):

Yutao Zhao ◽

Xiaoqian Liu ◽

Jing Ouyang ◽

Yan Wang ◽

Shanyu Xu ◽

...

Keyword(s):

Linear Models ◽

Nonlinear Models ◽

Gene Expression Programming ◽

Heuristic Method ◽

Quantitative Structure Activity Relationship ◽

Molecular Models ◽

Receptor Antagonists ◽

Ccr5 Receptor ◽

Self Consistent Field ◽

Qsar Models

Background: In this study, modulators of human Chemotactic cytokine receptor 5 (CCR5) were described using a quantitative structure-activity relationship model (QSAR). This model was based on the molecule’s chemical structure. Methods:: All 56 compounds of CCR5 receptor antagonists were randomly separated into two sets, 43 were reserved for training and the other 13 for testing. In the course of this study, molecular models were drawn using ChemDraw software. By means of Hyperchem software as well as optimized with both AM1 (semi-empirical self-consistent-field molecular orbital) and MM+ (molecular mechanics plus force field), molecular models were described through numerous descriptors using CODESSA software. Results: Linear models were obtained by Heuristic Method (HM) software and nonlinear models were obtained using APS software with optimal descriptor combinations used to build linear QSAR models, involving a group of selected descriptors. As a result, values of the above two different sets were shown to result from 0.82 in testing and 0.86 in training in HM while 0.83 in testing and 0.88 in training in Gene Expression Programming (GEP). Conclusion: From this method, the activity of molecules could be predicted, and the molecular structure could be changed to alter its IC50, avoiding the testing of large numbers of compounds.

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A Survey of Some Recent Results on Nonlinear Fault Tolerant Control

Mathematical Problems in Engineering ◽

10.1155/2010/586169 ◽

2010 ◽

Vol 2010 ◽

pp. 1-25 ◽

Cited By ~ 34

Author(s):

Mouhacine Benosman

Keyword(s):

Control Theory ◽

Linear Models ◽

Fault Tolerant ◽

Nonlinear Models ◽

Fault Tolerant Control ◽

Operating Life ◽

Linear And Nonlinear ◽

The One

Fault tolerant control (FTC) is the branch of control theory, dealing with the control of systems that become faulty during their operating life. Following the systems classification, as linear and nonlinear models, FTC can be classified in two different groups, linear FTC (LFTC) dealing with linear models, and the one of interest to us in this paper, nonlinear FTC (NFTC), which deals with nonlinear models. We present in this paper a survey of some of the results obtained in these last years on NFTC.

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Linear and nonlinear QSAR models of acute intravenous toxicity of organic chemicals for mice

Biochemistry (Moscow) Supplement Series B Biomedical Chemistry ◽

10.1134/s1990750811030103 ◽

2011 ◽

Vol 5 (3) ◽

pp. 213-225 ◽

Cited By ~ 3

Author(s):

O. A. Raevsky ◽

E. A. Liplavskaya ◽

A. V. Yarkov ◽

O. E. Raevskaya ◽

A. P. Worth

Keyword(s):

Organic Chemicals ◽

Qsar Models ◽

Linear And Nonlinear

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QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Journal of Cheminformatics ◽

10.1186/s13321-021-00508-0 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Amit Kumar Halder ◽

M. Natália Dias Soeiro Cordeiro

Keyword(s):

Open Source ◽

Linear Models ◽

Moving Average ◽

Biological Data ◽

Statistical Parameters ◽

Computational Tools ◽

Qsar Modelling ◽

Qsar Models ◽

Quantitative Structure Activity Relationships ◽

Set Up

AbstractQuantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

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Nonlinearities In Stock Return Prediction: Evidence From South Africa

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v28i6.7341 ◽

2012 ◽

Vol 28 (6) ◽

pp. 1253 ◽

Cited By ~ 1

Author(s):

Kathleen Hodnett ◽

Heng-Hsing Hsieh ◽

Paul Van Rensburg

Keyword(s):

Cross Section ◽

Linear Models ◽

Nonlinear Models ◽

Learning Rule ◽

Equity Returns ◽

Neural Network Modeling ◽

Font Size ◽

Stock Selection ◽

Linear And Nonlinear ◽

The Cross

This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear stock selection models are constructed based on the cross-section of equity returns with firm-specific attributes as model inputs. Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE. The risk-adjusted performance of the nonlinear models is found to be comparable with that of linear models. In terms of artificial neural network modeling, the extended Kalman filter learning rule is found to outperform the traditional backpropagation approach. This finding is consistent with our prior findings on global stock selection.

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