Multivariate statistical analysis methods in QSAR

Pirhadi, Somayeh; Shiri, Fereshteh; Ghasemi, Jahan B.

doi:10.1039/c5ra10729f

Cited by 77 publications

(35 citation statements)

References 197 publications

(200 reference statements)

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“…After conversion of the Flavor‐Base dataset into a binary matrix, the data were explored using statistical methods currently used in chemoinformatics to explore odorants and odors spaces . Due to the fuzzy nature of semantic odor descriptions we aimed to categorize the odorants and odor notes by implementing various methods.…”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Tromelin

Chabanet

Audouze

et al. 2017

Flavour & Fragrance J

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The perception of odor is an important component of smell; the first step of odor detection, and the discrimination of structurally diverse odorants depends on their interactions with olfactory receptors (ORs). Indeed, the perception of an odor's quality results from a combinatorial coding, in which the deciphering remains a major challenge.Several studies have successfully established links between odors and odorants by categorizing and classifying data. Hence, the categorization of odors appears to be a promising way to manage odors.In the proposed study, we performed a computational analysis using odor descriptions of the odorants present in Flavor-Base 9th Edition (2013). We converted the Flavor-Base data into a binary matrix (1 when the odor note appears in the odor description, 0 otherwise). We retained 251 odor notes and 3508 odorants, considering only the orthonasal perception. Two categorization methods were performed: agglomerative hierarchical clustering (AHC), and self-organizing map (SOM). AHC was based on a measure of the distance between the elements performed by multidimensional scaling (MDS) for the odorants, and correspondence analysis (CA) for the odor notes.The results demonstrated that the SOM classes appeared to be less dependent on the frequency of the odor notes than those of the AHC clusters. SOMs are especially useful for identifying the associations between less than 4 or 5 odor notes within groups of odorants.The obtained results highlight subsets of odorants sharing similar groups of odor notes, suggesting an interesting and promising way of using computational approaches to help decipher olfactory coding.

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Section: Introductionmentioning

confidence: 99%

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Tromelin

Chabanet

Audouze

et al. 2017

Flavour & Fragrance J

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“…8), 83 % of the model fits well. Also, the external validation metrics, Q 2 TEST , re-affirms the predictability and applicability of the model on a random and normally distributed data, (usually the test set) [10,11,13]. Since C000000956 falls within the applicability domain of the model, its IC50 was predicted to be 4.301 μM.…”

Section: Description Value Inferencementioning

confidence: 86%

“…QSAR modelling is a statistical approach for the identification of chemical variables that predict ligand bioactivity. These chemical variables (called molecular descriptors) have been found to be highly useful in the hit-to-lead optimization process of drug discovery [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Molecular Docking and 3D Qsar Studies of C000000956 as a Potent Inhibitor of Bace-1

et al. 2019

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Background BACE-1 is an aspartate protease that is responsible for the proteolysis of amyloid precursor proteins (APP) into beta-amyloid (Aβ), a neurotoxic peptide in patients with Alzheimer’s disease (AD). As such, BACE-1 is a prime pharmacological target in the control of Aβ in the brain and its inhibition will be a sound approach in AD therapy. Methods The computational pipeline which comprised molecular docking (MD), Quantitative Structure Activity Relationship (QSAR) modelling and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) studies enabled the prediction of molecular interaction and relative inhibitory potentials of the hit compound. Results and Discussion The current study reports a naturally sourced small molecule inhibitor of BACE1 (C000000956) which was obtained through a computational pipeline. Also, pharmacological constraints such as pH dependent activity of the enzyme and blood brain barrier permeation which have been associated with the efficacy of previous BACE-1 inhibitors were well catered for. Our results suggest that orally delivered C000000956 is a potential small molecule inhibitor of BACE-1 which may find usefulness in AD-therapy.

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“…Nevertheless, multiple linear regression (MLR) analysis is one of the most widely used [11][12][13][14][15][16][17][18] of all statistical tools. It is a prolongation of the linear regression where one response is linked to a number of independent variables, being used in a variety of circumstances: i) when it is known from theoretical considerations in the matter that the relationship follows that form; ii) or when the exact relationship connecting y and the x's, either is unknown or is too complicated to be used directly, being thus presumed than an approach of this kind is suitable.…”

Section: Introductionmentioning

confidence: 99%

Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications

Martín¹,

Navas²,

Fernandez-Recamales³

et al. 2017

International Journal of Advanced Research in Chemical Science

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Multivariate statistical analysis methods in QSAR

Abstract: The emphasis of this review is particularly on multivariate statistical methods currently used in quantitative structure–activity relationship (QSAR) studies.

Cited by 77 publications

References 197 publications

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Molecular Docking and 3D Qsar Studies of C000000956 as a Potent Inhibitor of Bace-1

Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications

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