QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling

Background: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors. Objective: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models. Methods: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database. Results: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models. Conclusion: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs.

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Molecular Shape Descriptors. 1. Three-Dimensional Molecular Shape Descriptor

Zeitschrift für Naturforschung A ◽

10.1515/zna-1985-1106 ◽

1985 ◽

Vol 40 (11) ◽

pp. 1108-1113 ◽

Cited By ~ 9

Author(s):

I. Motoc ◽

G. R. Marshall ◽

R. A. Dammkoehler ◽

J. Labanowski

Keyword(s):

Numerical Integration ◽

Van Der Waals ◽

Three Dimensional ◽

Shape Descriptor ◽

Molecular Shape ◽

Shape Descriptors ◽

Van Der Waals Volume

The paper presents and illustrates a method which uses numerical integration of the van der Waals envelope(s) to calculate with desired accuracy the molecular van der Waals volume and the three-dimensional molecular shape descriptor defined as the twin-number [OV(α, β); NOV(β, α), where OV and NOV represent the overlapping and, respectively, the nonoverlapping van der Waals volumes of the molecules α and ß superimposed according to appropriate criteria.

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CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

Open Chemistry ◽

10.2478/s11532-009-0073-4 ◽

2009 ◽

Vol 7 (4) ◽

pp. 909-922 ◽

Cited By ~ 3

Author(s):

Brij Sharma ◽

Pradeep Pilania ◽

Prithvi Singh ◽

Susheela Sharma ◽

Yenamandra Prabhakar

Keyword(s):

Van Der Waals ◽

Distance Matrix ◽

Computational Procedure ◽

Carbonic Anhydrases ◽

Inhibition Activity ◽

Qsar Study ◽

Van Der Waals Volume ◽

Content Index ◽

Dragon Descriptors ◽

Qsar Models

AbstractThe inhibition activities of sulfonamide and sulfamate derivatives for human carbonic anhydrases have been quantitatively analyzed using DRAGON descriptors. QSAR models have been obtained through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. For the hCA I inhibition activity, a higher value of information content index of the 1-order neighborhood symmetry (IC1) and a lower value of the Moran autocorrelations, MATS2v and MATS1p, along with a lower number of sulfur atoms in a molecular structure (nRSR) is beneficial to the activity. A higher number of 5-membered rings (nR05), a bigger distance between nitrogen and sulfur T(N..S), and a higher value of van der Waals volume weighted descriptor (GATS6v), are helpful to improve the hCA II inhibition activity. For the inhibition of hpCA, a lower value of the descriptors Jhetv and PW5, and higher values of the eigenvalue sum from Z weighted distance matrix, SEigZ, the Moran autocorrelation of lag 8 weighted by atomic van der Waals volumes, MATS8v and the Moran autocorrelation of lag 4 weighted by atomic Sanderson electronegativities, MATS4e are favorable. The derived significant models in such descriptors may further be used to synthesize new potential compounds and to decipher the mode of their actions at molecular level.

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DESIGN OF NEW PEROXISOME PROLIFERATORS GAMMA ACTIVATED RECEPTOR AGONISTS (PPARγ) VIA QSAR BASED MODELING

Journal of Applied Pharmaceutical Sciences and Research ◽

10.31069/japsr.v1i01.13059 ◽

2018 ◽

pp. 23-26 ◽

Cited By ~ 1

Author(s):

Tripathi RB ◽

Jain J ◽

Siddiqui AW

Keyword(s):

Molecular Descriptor ◽

Binding Pocket ◽

Peroxisome Proliferators ◽

Qsar Study ◽

Qsar Analysis ◽

Qsar Models ◽

Artery Disease ◽

Peroxisome Proliferators Activated Receptors ◽

Recent Attention

The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zincfinger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseasessuch as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators havedrawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize newPPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improveglucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint toimprove the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSARsoftware (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysisand calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSARsoftware (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitativestructure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activatedreceptor agonists (PPARγ).The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters.Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.

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QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207321666180213092352 ◽

2018 ◽

Vol 21 (3) ◽

pp. 204-214 ◽

Cited By ~ 3

Author(s):

Vesna Rastija ◽

Maja Molnar ◽

Tena Siladi ◽

Vijay Hariram Masand

Keyword(s):

Antioxidant Activity ◽

Antioxidant Activities ◽

Dipicolinic Acid ◽

External Validation ◽

Qsar Model ◽

Internal Validation ◽

Qsar Analysis ◽

Dragon Descriptors ◽

Qsar Models ◽

New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

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Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190419111559 ◽

2019 ◽

Vol 22 (5) ◽

pp. 333-345

Author(s):

Morteza Rezaei ◽

Esmat Mohammadinasab ◽

Tahere Momeni Esfahani

Keyword(s):

Density Functional ◽

Pharmaceutical Compounds ◽

Partial Least Square ◽

Least Square ◽

Basis Set ◽

Van Der Waals Volume ◽

Relationship Analysis ◽

Aniline Derivatives ◽

Aqueous Solvent ◽

Qsar Models

Background: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds. Objective: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models. Materials & Methods: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds. Results: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models. Conclusion: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

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QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

Letters in Drug Design & Discovery ◽

10.2174/1570180815666181004112947 ◽

2020 ◽

Vol 17 (2) ◽

pp. 214-225 ◽

Cited By ~ 3

Author(s):

Piotr Kawczak ◽

Leszek Bober ◽

Tomasz Bączek

Keyword(s):

Antimicrobial Activity ◽

Heterocyclic Compounds ◽

Molecular Descriptors ◽

Microbiological Activity ◽

Activity Data ◽

Qsar Analysis ◽

Qsar Models ◽

Nitro Derivatives ◽

Semi Empirical ◽

Derivatives Of

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

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