An evaluation of existing QSAR models and structural alerts and development of new ensemble models for genotoxicity using a newly compiled experimental dataset

2021 ◽  
Vol 18 ◽  
pp. 100167
Author(s):  
Prachi Pradeep ◽  
Richard Judson ◽  
David M. DeMarini ◽  
Nagalakshmi Keshava ◽  
Todd M. Martin ◽  
...  
Keyword(s):  
Experimental Dataset ◽  
Ensemble Models ◽  
Qsar Models ◽  
Structural Alerts

scholarly journals Alarms about structural alerts

2016 ◽  
Vol 18 (16) ◽  
pp. 4348-4360 ◽  
Author(s):  
Vinicius M. Alves ◽  
Eugene N. Muratov ◽  
Stephen J. Capuzzi ◽  
Regina Politi ◽  
Yen Low ◽  
...  
Keyword(s):  
Safety Assessment ◽  
Integrative Approach ◽  
Qsar Models ◽  
Structural Alerts

Integrative approach for safety assessment of new chemicals by combining structural alerts and QSAR models.

scholarly journals Mutagenic and carcinogenic structural alerts and their mechanisms of action

2016 ◽  
Vol 67 (3) ◽  
pp. 169-182 ◽  
Author(s):  
Alja Plošnik ◽  
Marjan Vračko ◽  
Marija Sollner Dolenc
Keyword(s):  
Risk Assessment ◽  
Chemical Compounds ◽  
Mechanisms Of Action ◽  
Classification Schemes ◽  
Epigenetic Effects ◽  
Qsar Models ◽  
Carcinogenic Effects ◽  
Hazard And Risk ◽  
Structural Alerts ◽  
Hazard And Risk Assessment

Abstract Knowing the mutagenic and carcinogenic properties of chemicals is very important for their hazard (and risk) assessment. One of the crucial events that trigger genotoxic and sometimes carcinogenic effects is the forming of adducts between chemical compounds and nucleic acids and histones. This review takes a look at the mechanisms related to specific functional groups (structural alerts or toxicophores) that may trigger genotoxic or epigenetic effects in the cells. We present up-to-date information about defined structural alerts with their mechanisms and the software based on this knowledge (QSAR models and classification schemes).

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

2018 ◽  
pp. 23-26 ◽  
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.

Classification-based QSAR Models for the Prediction of the Bioactivity of ACE-inhibitor Peptides

2018 ◽  
Vol 25 (11) ◽  
pp. 1015-1023 ◽  
Author(s):  
Piercosimo Tripaldi ◽  
Andrés Pérez-González ◽  
Cristian Rojas ◽  
Johann Radax ◽  
Davide Ballabio ◽  
...  
Keyword(s):  
Ace Inhibitor ◽  
Qsar Models

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
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.

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

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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