Multivariate Linear QSPR/Qsar Models: Rigorous Evaluation of Variable Selection for PLS

Вармуза, Курт; Filzmoser, Peter; Dehmer, Matthias

doi:10.5936/csbj.201302007

Cited by 33 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SpMax3_Bh(m) has been used in predicting depuration rate constants for environmental pollutants of the polychlorinated biphenyls group (87), and the less relevant (in our case) SpMax6_Bh(m) has been used to predict chronic toxicity of substances to Pseudokirchneriella subcapitata (88). The second most important descriptor for our data set was DECC (eccentric topologic index) has been previously reported to be important in the prediction of MAO-A activity (89,90) , placental barrier permeability (91), and gas chromatographic retention times (92). F06 [C-N] was used in a model to describe the anti-proliferative effect of phenyl 4-(2-oxoimidazolidin-1yl)-benzenesulfonates (local QSAR model) (93), anti-malaric effect (94), or skin permeability of substances (95).…”

Section: Discussionmentioning

confidence: 98%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

show abstract

Section: Discussionmentioning

confidence: 98%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The QSAR models are built using multiple algorithms. Independent input variables are sets of quantitative characteristics of molecules included in the studies and called descriptors. They represent topological properties of molecular graphs, describing three‐dimensional molecular fields and interactions with target proteins, just physicochemical properties, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Dependent input variables are biological activities, toxicities and other properties which the models should be able to predict with sufficiently high accuracy. There are other steps of preprocessing and curation of descriptors not discussed here in detail . The models are built using different QSAR methodologies (for example, see the publication by K. Varmuza et al …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Value of p‐Value

Gelbukh

2019

Molecular Informatics

View full text Add to dashboard Cite

The goal of this manuscript is to discuss important aspects of external validation of classification and category Quantitative Structure -Activity/Property/Toxicity Relationship QS/A/P/T/R models that to the best of author's knowledge are not addressed in publications. Statistical significance (in terms of p-value) and accuracy of prediction (in terms of Correct Classification Rate (CCR)) of external validation set compounds are among most important characteristics of the models. We assert that in most cases the models built for classification or category response variable should be statistically significant and predictive for each class or category. We show that three thresholds of the number of compounds in each class or category of the external validation sets should be satisfied. 1) The p-value criterion can never be satisfied, if the number of compounds is below the first threshold. 2) If the number of compounds is between the first and the second thresholds, p-value criterion should be used. 3) If it is higher than the third threshold, classification or category accuracy criterion should be used. 4) If the number of compounds is between second and third thresholds, either one or the other criterion should be used depending on the value of p-value. 5) When the number of compounds in the class approaches infinity, the maximum relative error of prediction approaches the relative expected error. The results are of interest in other areas of multidimensional data analysis.Keywords: external validation of QSAR models · classification and category response variables · p-values and correct classification rate as alternative criteria of prediction accuracy[a] A. Golbraikh

show abstract

“…PLS algorithm was chosen since it is largely used in medicinal chemistry [17]. Machine learning tools on the other hand are largely used by biologists and have also been shown to have potential utility in the modelling of pharmaceutical problems [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction and interpretation of the lipophilicity of small peptides

Visconti

Ermondi

Esposito

2015

J Comput Aided Mol Des

View full text Add to dashboard Cite

Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict log D oct of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict log D oct of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

show abstract

Multivariate Linear QSPR/Qsar Models: Rigorous Evaluation of Variable Selection for PLS

Cited by 33 publications

References 24 publications

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Value of p‐Value

Prediction and interpretation of the lipophilicity of small peptides

Contact Info

Product

Resources

About