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.

QSAR Modeling and Prediction of Triptan Binding Affinities

2021 ◽  
Vol 6 (2) ◽  
pp. 19-28
Author(s):  
Lucas Alland ◽  
Solomon H. Jacobson
Keyword(s):  
Neural Network ◽  
Binding Affinities ◽  
Potential Drug ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Drug Candidates ◽  
Modeling And Prediction ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

The purpose of this study was to use quantitative structure-activity relationships (QSARs) to identify new triptan molecules that selectively bind to h 5-HT1B and h 5-HT1D to a greater extent than to the similar h 5-HT1A receptor in order to identify novel compounds that could lead to an alternative and potentially superior migraine relief drug. Due to its possibility in migraine abortive properties, binding affinities to h 5-HT1F were also modeled. Binding affinities for 12 different triptan drugs and structurally similar substances were compiled from the literature, and descriptors were generated for those and other potential drug candidates using a variety of programs. The most significant descriptors were identified using a stepwise model, and the final QSARs were built for each activity with those descriptors, and a neural network. QSARs for all four activities were validated using a holdback method and were all found to be highly accurate. With these QSARs, activities of novel compounds similar to triptan drugs were predicted and three potential drug candidates were suggested.

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