scholarly journals New Antitrichomonal Drug-like Chemicals Selected by Bond (Edge)-Based TOMOCOMD-CARDD Descriptors

2008 ◽  
Vol 13 (8) ◽  
pp. 785-794 ◽  
Author(s):  
Alfredo Meneses-Marcel ◽  
Oscar M. Rivera-Borroto ◽  
Yovani Marrero-Ponce ◽  
Alina Montero ◽  
Yanetsy Machado Tugores ◽  
...  
Keyword(s):  
Wistar Rats ◽  
Molecular Descriptors ◽  
Correlation Coefficients ◽  
Linear Discriminant ◽  
Structure Activity ◽  
Qsar Models ◽  
Test Sets ◽  
Quantitative Structure Activity Relationships ◽  
Edge Based

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 μg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 μg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 μg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound. (Journal of Biomolecular Screening 2008:785-794).

Structure-Activity Considerations in Risk Assessment: a Simulation Study

1985 ◽  
Vol 1 (4) ◽  
pp. 249-259 ◽  
Author(s):  
Steven R. Lavenhar ◽  
Carol A. Maczka
Keyword(s):  
Aromatic Amines ◽  
Molecular Descriptors ◽  
Carcinogenic Risk ◽  
Data Set ◽  
Multistage Model ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

The use of quantitative structure-activity relationships (QSAR) is considered with respect to estimating the carcinogenic risk of untested chemicals. SAR derived from a retrospective classification of a series of aromatic amines were used to study the estimation of carcinogenic risk by analogy. Using pattern recognition methods, a series of molecular descriptors were developed for a data set of aromatic amines that supported a linear discriminant function capable of separating compounds testing positively for carcinogenicity from those testing negatively. Linear discriminant analysis correctly categorized the compounds as positive or negative in 94.9% of the cases. For each aromatic amine within the subset of positive compounds, the most appropriate analogue was identified using physicochemical, topological, geometric and electronic molecular descriptors as variables. An upper-limit unit risk estimate was calculated for each compound that was a positive carcinogen within the data set using the linearized multistage model. The actual risk and the risk estimated by analogy to a congener were compared for each compound within the positive subset. The results support estimating both qualitative and quantitative carcinogenic risk by analogy for this particular data set.

scholarly journals The effect of noise on the predictive limit of QSAR models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Scott S. Kolmar ◽  
Christopher M. Grulke
Keyword(s):  
Random Error ◽  
Experimental Error ◽  
Computational Models ◽  
Model Performance ◽  
Training Data ◽  
Structure Activity ◽  
Qsar Models ◽  
Data Points ◽  
Test Sets ◽  
Quantitative Structure Activity Relationships

AbstractA key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology. Graphical Abstract

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

2013 ◽  
pp. 322-427
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Classification Models ◽  
Training Set ◽  
Linear Discriminant ◽  
Oecd Principles ◽  
Qsar Models ◽  
Validation Set ◽  
Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

Quantitative Structure Activity Relationships of Fungicidally Active Triazoles: Analogs and Stereoisomers of Propiconazole and Etaconazole Quantitative Structure Activity Relationships of Fungicidally Active Triazoles: Analogs and Stereoisomers of Propiconazole and Etaconazole

1989 ◽  
Vol 44 (1-2) ◽  
pp. 85-96 ◽  
Author(s):  
E. Ebert ◽  
W. Eckhardt ◽  
K. Jäkel ◽  
D. Sozzi ◽  
C. Vogel ◽  
...  
Keyword(s):  
Fungicidal Activity ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

Abstract The preparation of the four stereoisomers of propiconazole (TILT®) is described. Their inhibition of the 14α-C-demethylation of the sterol nucleus is examined and compared with the inhibition by the four stereoisomers of etaconazole (SONAX®). The quantitative structure-activity relationships (QSAR) of substituted 1,3-dioxolane-2-yl-methyltriazoles and 1,3-dioxane-2-ylmethyltriazoles on in vivo fungicidal activity are investigated.

An Evaluation of the Predictive Ability of the QSAR Software Packages, DEREK, HAZARDEXPERT and TOPKAT, to Describe Chemically-induced Skin Irritation

2008 ◽  
Vol 36 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Enrico Mombelli
Keyword(s):  
Skin Irritation ◽  
Predictive Ability ◽  
Computational Techniques ◽  
Animal Testing ◽  
Software Packages ◽  
Structure Activity ◽  
Chemically Induced ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
The Eu

According to the REACH chemicals legislation, formally adopted by the EU in 2006, Quantitative Structure–Activity Relationships (QSARs) can be used as alternatives to animal testing, which itself poses specific ethical and economical concerns. A critical assessment of the performance of the QSAR models is therefore the first step toward the reliable use of such computational techniques. This article reports the performance of the skin irritation module of three commercially-available software packages: DEREK, HAZARDEXPERT and TOPKAT. Their performances were tested on the basis of data published in the literature, for 116 chemicals. The results of this study show that only TOPKAT was able to predict the irritative potential for the majority of chemicals, whereas DEREK and HAZARDEXPERT could correctly identify only a few irritant substances.

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 Validation of QSAR models for legislative purposes

2009 ◽  
Vol 2 (3) ◽  
pp. 184-186 ◽  
Author(s):  
Miloň Tichý ◽  
Marián Rucki
Keyword(s):  
Oecd Countries ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Oecd Principles ◽  
The World ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

Validation of QSAR models for legislative purposesOECD principles of validation of Quantitative Structure - Activity Relationships (QSAR) models for legislative purposes are given and explained. Reasons of their origination and development, like system REACH, are described. A basic impulse has come from some OECD countries followed by all (almost) other countries of the world.

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

2017 ◽  
Vol 16 (05) ◽  
pp. 1750038 ◽  
Author(s):  
Abolfazl Barzegar ◽  
Hossein Hamidi
Keyword(s):  
Therapeutic Agents ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Qsar Models ◽  
Stepwise Procedure ◽  
Quantitative Structure Activity Relationships ◽  
Anti Hiv ◽  
Hiv 1 ◽  
Mlr Model

Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure–activity relationships (QSARs) algorithms have been used in order to develop efficient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA–MLR and stepwise PCA–MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the “concrete” and “stepwise” procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA–MLR model showed that the method has higher ability to predict the compounds’ activity. The stepwise PCA–MLR model showed highly validated statistical results both in fitting and prediction processes ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient descriptors for building good predictability stepwise PCA–MLR. The stepwise hybrid approach of PCA–MLR may be useful in derivation of highly predictive and interpretable QSAR models.

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