QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals

2018 ◽  
Vol 20 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Paola Gramatica ◽  
Ester Papa ◽  
Alessandro Sangion
Keyword(s):  
Hazardous Chemicals ◽  
Qsar Modeling ◽  
End Points ◽  
Qsar Models

Indexes for the prioritization of potential hazardous chemicals can be derived and modelled by combining PCA and QSAR models.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

Nanoscale ◽  
2016 ◽  
Vol 8 (13) ◽  
pp. 7203-7208 ◽  
Author(s):  
Natalia Sizochenko ◽  
Agnieszka Gajewicz ◽  
Jerzy Leszczynski ◽  
Tomasz Puzyn
Keyword(s):  
Causal Inference ◽  
Quality Evaluation ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Validation Criteria ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
Inference Methods

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure–Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model.

scholarly journals Rodent Carcinogenicity Dataset

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Natalja Fjodorova ◽  
Marjana Novič
Keyword(s):  
Molecular Structure ◽  
Quantitative Structure Activity Relationship ◽  
Future Research ◽  
Qsar Modeling ◽  
Carcinogenic Potency ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
Funded Project ◽  
Rodent Carcinogenicity

The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information: common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment.

scholarly journals A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multilinear Regression ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Antimalarial Agents ◽  
Inhibition Concentration ◽  
Qsar Models ◽  
Semi Empirical ◽  
Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

scholarly journals QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5270
Author(s):  
Yangxi Yu ◽  
Hiep Dong ◽  
Youyi Peng ◽  
William J. Welsh
Keyword(s):  
Binding Affinity ◽  
Model Building ◽  
Present Report ◽  
Antidepressant Drug ◽  
Biological Evaluation ◽  
Qsar Modeling ◽  
Therapeutic Drugs ◽  
First Case ◽  
Qsar Models ◽  
Approved Drugs

S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer’s disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.

scholarly journals Principles of QSAR Modeling

2020 ◽  
Vol 5 (3) ◽  
pp. 61-97 ◽  
Author(s):  
Paola Gramatica
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Academic Career ◽  
Environmental Pollutants ◽  
Ordinary Least Squares ◽  
Qsar Modeling ◽  
Qsar Models

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

2D- and 3D-QSAR Modeling of Imidazole-Based Glutaminyl Cyclase Inhibitors

2019 ◽  
Vol 15 ◽  
Author(s):  
Omar Husham Ahmed Al-Attraqchi ◽  
Katharigatta N. Venugopala
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Virtual Screening ◽  
Neurodegenerative Disorder ◽  
3D Qsar ◽  
Biological Evaluation ◽  
Qsar Modeling ◽  
Glutaminyl Cyclase ◽  
Validation Parameters ◽  
Qsar Models

Background: Glutaminyl cyclase (QC) is a novel target in the battle against Alzheimer’s disease, a highly prevalent neurodegenerative disorder. QC inhibitors have the potential to be developed as therapeutically useful anti-Alzheimer’s disease agents. Methods: Linear and non-linear 2D-quantitative structure–activity relationship (QSAR) models were developed using stepwise-multiple linear regression (S-MLR) and neural networks. Partial least squares (PLS) method was used to develop a 3D-QSAR model. Also, the developed models were used in a virtual screening of the ZINC database to identify potential QC inhibitors. Results: The 2D neural network model showed superior predictive ability, as demonstrated by the validation parameters R2 = 0.933, Q2 = 0.886 and R2pred = 0.911. The 3D-QSAR model’s steric and electrostatic fields’ isocontour maps were visualized and revealed important structural requirements associated with good activity. The virtual screening identified six compounds as potentially active QC inhibitors with improved pharmacokinetic profiles. Conclusion: The developed QSAR models can be used to predict the activity of compounds not yet synthesized and prioritize their synthesis and biological evaluation. Also, potentially active QC inhibitors have been identified with attractive lead-like properties that can be used to develop anti-Alzheimer’s disease agents.

scholarly journals QSAR-modeling of desoxyuridine triphosphatase inhibitors in a series of some derivatives of uracil

2019 ◽  
Vol 65 (2) ◽  
pp. 103-113
Author(s):  
Yu.Z. Martynova ◽  
V.R. Khairullina ◽  
A.R. Gimadieva ◽  
A.G. Mustafin
Keyword(s):  
Practical Application ◽  
Qsar Modeling ◽  
Dna Biosynthesis ◽  
Promising Target ◽  
Wide Range ◽  
Consensus Models ◽  
Qsar Models ◽  
Selection Stage ◽  
The Relationship ◽  
Derivatives Of

Due to the widespread prevalence, deoxyuridine triphosphatase (UTPase) is considered by modern biochemists and physicians as a promising target for the development of drugs with a wide range of activities. The therapeutic effect of these drugs will be due to suppression of DNA biosynthesis in various viruses, bacteria and protozoa. In order to rationalize the search for new dUTPase inhibitors, domestic and foreign researchers are actively using the QSAR methodology at the selection stage of hit compounds. However, the practical application of this methodology is impossible without existence of valid QSAR models. With the use of the GUSAR 2013 program, a quantitative analysis of the relationship between the structure and efficacy of 135 dUTPase inhibitors based on uracil derivatives was performed in the IC50 range of 30¸185000 nmol/L. Six statistically significant valid consensus models, characterized by high descriptive ability and moderate prognostic ability on the structures of training and test samples, are constructed. To build valid QSAR models for dUTPase inhibitors can use QNA or MNA descriptors and their combinations in a consensus approach.

scholarly journals Structure Activity Relationships, QSAR Modeling and Drug-Like Calculations of TP Inhibition of 1,3,4-Oxadiazoline-2-Thione Derivatives

2014 ◽  
Vol 37 ◽  
pp. 113-124 ◽  
Author(s):  
Zineb Almi ◽  
Salah Belaidi ◽  
Touhami Lanez ◽  
Noureddine Tchouar
Keyword(s):  
Predictive Power ◽  
Cross Validation ◽  
Molecular Descriptor ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Structure Activity ◽  
Qsar Models ◽  
Leave One Out

QSAR studies have been performed on twenty-one molecules of 1,3,4-oxadiazoline-2-thiones. The compounds used are among the most thymidine phosphorylase (TP) inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and TP inhibition of the 1,3,4-oxadiazoline-2-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, Pol, MR, MV, and MW, qO1, SAG, for the TP inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.

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