QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Darnag, Rachid; Minaoui, Brahim; Fakir, Mohamed

doi:10.1016/j.arabjc.2012.10.021

Cited by 47 publications

(27 citation statements)

References 29 publications

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“…There is no explicit prediction equation that can be reported, rather there is a computerized decision tree into which any number of proteins (each represented by its own set of molecular properties) can be entered to generate a predicted elution profile. 12,42 There are numerous other chemometric methods in the literature including partial least squares, 11,43 principal component analysis, 44,45 artificial neural network, 46,47 support vector machines, 22,23,[48][49][50] and multiple linear regression. 47,50 The MVRF methodology was chosen over these other options in part because of the ease in which it can handle data nonlinearities as well as complex response vectors.…”

Section: Previous Methodsmentioning

confidence: 99%

Accounting for host cell protein behavior in anion‐exchange chromatography

Swanson

Nettleton

et al. 2016

Biotechnology Progress

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Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0. (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning (ATPS) followed by two-dimensional electrophoresis (2DE) provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error (RMSE) value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX.

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Section: Previous Methodsmentioning

confidence: 99%

Accounting for host cell protein behavior in anion‐exchange chromatography

Swanson

Nettleton

et al. 2016

Biotechnology Progress

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“…In summary, current modeling approaches are not capable of effective and comprehensive simulation of the hysteresis characteristics of an IPMC actuator, resulting in high costs of the control systems. The support vector machine (SVM), based on statistical theory and structuralriskminimization principle, outperforms the artificial neural network in terms of global optimization and generalization capability [36] and it shows good performance in hysteresis modeling [37,38]. As an extension of SVM, the leastsquares support vector machine (LSSVM) overcomes the defect of slow trainingspeed in SVM by solving a linear equation set rather than aquadratic optimization problem [39].…”

Section: Controlmentioning

confidence: 99%

Modeling and Control of IPMC Actuators Based on LSSVM-NARX Paradigm

Huang

Zhao³

et al. 2019

Mathematics

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Ionic polymer-metal composites are electrically driven intelligent composites that are readily exposed to bending deformations in the presence of external electric fields. Owing to their advantages, ionicpolymer-metal composites are promising candidates for actuators. However, ionicpolymer-metal composites exhibit strong nonlinear properties, especially hysteresis characteristics, resulting in severely reduced control accuracy. This study proposes an ionic polymer-metal composite platform and investigates its modeling and control. First, the hysteresis characteristics of the proposed Pt-electrode ionic polymer-metal composite are tested. Based on the hysteresis characteristics, ionic polymer-metal composites are modeled using the Prandtl-Ishlinskii model and the least squares support vector machine-nonlinear autoregressive model, respectively. Then, the ionic polymer-metal composite is driven by a random sinusoidal voltage, and the LSSVM-NARX model is established on the basis of the displacement data obtained. In addition, an artificial bee colony algorithm is proposed for accuracy optimization of the model parameters. Finally, an inverse controller based on the least squares support vector machine-nonlinear autoregressive model is proposed to compensate the hysteresis characteristics of the ionic polymer-metal composite. A hybrid PID feedback controller is developed by combining the inverse controller with PID feedback control, followed by simulation and testing of its actual position control on the ionic polymer-metal composite platform. The results show that the hybrid PID feedback control system can effectively eliminate the effects of the hysteresis characteristics on ionic polymer-metal composite control.

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“…In the literature, different methodologies are introduced for predicting the toxicity degree of chemical compounds. The most common methods are based on statistical analysis to discover the major associations among variables, that is, latent variables to form the covariance layouts in these spaces (Askari, Ghaedi, Dashtian, & Azghandi, ; Darnag, Minaoui, & Fakir, ). Despite the simplicity of these methods, there are certain restrictions and assumptions such as the independence of the variables and inherent normal distributions of the variables.…”

Section: Related Workmentioning

confidence: 99%

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

2019

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Cheminformatics plays a vital role in maintaining large amounts of chemical data. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State‐of‐the‐art prediction methods such as neural networks and multilayer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a quantum‐inspired genetic programming model to improve prediction accuracy. Genetic programming is utilized to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best‐of‐run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the quantum‐inspired genetic programming model has better goodness‐of‐fit statistics then and significantly outperforms the neural network model.

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QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Cited by 47 publications

References 29 publications

Accounting for host cell protein behavior in anion‐exchange chromatography

Accounting for host cell protein behavior in anion‐exchange chromatography

Modeling and Control of IPMC Actuators Based on LSSVM-NARX Paradigm

Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

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