Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Dudek, Arkadiusz Z.; Arodź, Tomasz; Gálvez, Jorge

doi:10.2174/138620706776055539

Cited by 359 publications

(260 citation statements)

References 144 publications

(189 reference statements)

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“…13 The main aim in QSRR analysis is to reduce the number of variables and to detect structure in the relationships between variables, by various statistical methods of explorative analysis, classification methods and regression methods. 17,18 PCA is a useful statistical technique for reducing the amount of data when there is correlation present, retaining as much as information as possible. This statistical technique calculates new, latent variables by a combination of the original variables.…”

Section: Chemometric Methodsmentioning

confidence: 99%

QSRR Analysis in Characterization of Some Benzimidazole Derivatives

Karadžić

Podunavac‐Kuzmanović

Jevrić

et al. 2015

ACSi

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In this paper, quantitative structure-retention relationship study has been applied in order to correlate obtained retention parameter R M 0 and two groups of molecular descriptors, for eleven investigated benzimidazole derivatives. Principal component analysis (PCA), followed by hierarchical cluster analysis (HCA), linear regression (LR) and multiple linear regression (MLR), was applied in order to identify the most important molecular descriptors. Mathematical models were established and the best models were further validated by leave-on-out (LOO) technique as well as by the calculation of the statistical parameters. Statistically significant models were established.

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Section: Chemometric Methodsmentioning

confidence: 99%

QSRR Analysis in Characterization of Some Benzimidazole Derivatives

Karadžić

Podunavac‐Kuzmanović

Jevrić

et al. 2015

ACSi

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“…2) Pre-processing the dataset: Normally not all the parameters in a dataset contribute towards an efficient model building process [13]. The key idea behind screening the bestfit features is to reduce the computation time of the model and decrease the dimensionality of the dataset.…”

Section: ) Preparation Of Datamentioning

confidence: 99%

A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Hassan¹,

Khan²

2017

ijacsa

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Abstract-Clinical studies in the past have shown that the pathology of Alzheimer's disease (AD) initiates, 10 to 15 years before the visible clinical symptoms of cognitive impairment starts to appear in AD diagnosed patients. Therefore, early diagnosis of the AD using potential early stage cerebrospinal fluid (CSF) biomarkers will be valuable in designing a clinical trial and proper care of AD patients. Therefore, the goal of our study was to generate a classification model to predict earlier stages of the AD using specific early-stage CSF biomarkers obtained from a clinical Alzheimer dataset. The dataset was segmented into variable sizes and classification models based on three machine learning (ML) algorithms, such as Sequential Minimal Optimization (SMO), Naïve Bayes (NB), and J48 were generated. The efficacy of the models to accurately predict the cognitive impairment status was evaluated and compared using various model performance parameters available in Weka software tool. The current findings show that J48 based classification model can be effectively employed for classifying cognitive impaired Alzheimer patient from normal healthy individuals with an accuracy of 98.82%, area under curve (AUC) value of 0.992 and sensitivity & specificity of 99.19% and 97.87%, respectively. The sample size (60% training and 40% independent test data) showed significant improvement in T-test with J48 algorithm when compared with other classifiers tested on Alzheimer dataset.

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“…Virtual filtering can eliminate compounds with predicted toxic of poor pharmacokinetic properties early in the pipeline. The chemoinformatic methods used in building QSAR models can be divided into three groups, i.e., extracting descriptors from molecular structure, choosing those informative in the context of the analyzed activity, and, finally, using the values of the descriptors as independent variables to define a mapping that correlates them with the activity in question [48] In 1996 Huibers et al [49] propose a general threeparameter structure -property relationship was developed for a diverse set of 77 nonionic surfactants, employing topological descriptors calculated for the hydrophobic fragment of the surfactant molecule. The three descriptors represent contributions from the size of the hydrophobic group, the size of the hydrophilic group, and the structural complexity of the hydrophobic group.…”

Section: Quantitative Structure Activity Rela-tionship (Qsar) Studiesmentioning

confidence: 99%

Self-Assembly Drugs: From Micelles to Nanomedicine

Messina¹,

Besada-Porto²,

Ruso

2014

CTMC

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Self-assembly has fascinated many scientists over the past few decades. Rapid advances and widespread interest in the study of this subject has led to the synthesis of an ever-increasing number of elegant and intricate functional structures with sizes that approach nano-and mesoscopic dimensions. Today, it has grown into a mature field of modern science whose interfaces with many disciplines have provided invaluable opportunities for crossing boundaries for scientists seeking to design novel molecular materials exhibiting unusual properties, and for researchers investigating the structure and function of biomolecules. Consequently, self-assembly transcends the traditional divisional boundaries of science and represents a highly interdisciplinary field including nanotechnology and nanomedicine. Basically, self-assembly focuses on a wide range of discrete molecules or molecular assemblies and uses physical transformations to achieve its goals. In this Review, we present a comprehensive overview of the advances in the field of drug self-assembly and discuss in detail the synthesis, self-assembly behavior, and physical properties as well as applications. We refer the reader to past reviews dealing with colloidal molecules and colloidal self-assembly. In the first part, we will discuss, compare, and link the various bioinformatic procedures: Molecular Dynamics and Quantitative Structure Activity Relationship. The second section deals with the self-assembly behavior in more detail, in which we focus on several experimental techniques, selected according to the depth of knowledge obtained. The last part will review the advances in drug-protein assembly. Nature provides many examples of proteins that form their substrate binding sites by bringing together the component pieces in a process of self-assembly. We will focus in the understanding of physical properties and applications developing thereof.

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Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Cited by 359 publications

References 144 publications

QSRR Analysis in Characterization of Some Benzimidazole Derivatives

QSRR Analysis in Characterization of Some Benzimidazole Derivatives

A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Self-Assembly Drugs: From Micelles to Nanomedicine

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