Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

Duchowicz, Pablo R.

doi:10.3390/cells7020013

Cited by 20 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main advantage of not considering molecular conformations is that the only experimental information required to establish the QSAR models is the biological activity that is analyzed (Duchowicz 2018), in our case the antioxidant activity. No additional experimental information was required, such as that provided, for example, by X-ray structural crystallographic data for some anthocyanin conformation in its molecular environment, data that are often unknown.…”

Section: Resultsmentioning

confidence: 99%

QSAR studies of the antioxidant activity of anthocyanins

2019

View full text Add to dashboard Cite

Through experimental information available from antioxidant assays of seventeen anthocyanins, and six common anthocyanidins, quantitative structure-activity relationships (QSAR) have been established in the present work. The antioxidant bioactivity has been predicted in three different lipid environments: emulsified and bulk oil (methyl linoleate) (in vitro tests) at concentrations of 50 and 250 lM, and 50 lM of the inhibitor, respectively, and in human LDL (low-density lipoprotein; ''bad cholesterol'') (ex vivo test) at concentrations of 2.5, 10, and 25 lM of the inhibitor. Radical scavenging activity was predicted in the assay with the 1,1-diphenyl-2-picrylhydrazyl radical (DPPHÁ). The QSAR models developed for each test and concentration used allowed to obtain prospective information on the constitutional and topological molecular characteristics for anthocyanin/anthocyanidin compounds. Therefore, the antioxidant activity was predicted for twenty-one compounds with unknown experimental values, leading for some of them to a favorable predicted bioactivity.

show abstract

Section: Resultsmentioning

confidence: 99%

QSAR studies of the antioxidant activity of anthocyanins

2019

View full text Add to dashboard Cite

show abstract

“…Regression models, which are statistical models, can be broadly classified as linear regression and nonlinear regression. These include linear, multivariate linear, polynomial, stepwise, ridge, and least absolute shrinkage and selection operator. − These regression models are used for quantitative predictions unlike the standard classification models. Examples of these quantitative characteristics of toxicity include LD 50 , LC 50 , IC 50 , and EC 50 .…”

Section: Model Typesmentioning

confidence: 99%

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

Wang

Goodman

Allen

2020

Chem. Res. Toxicol.

View full text Add to dashboard Cite

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure−activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, nai ̈ve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

show abstract

“… 8 − 10 As objective end points of QSAR models, the half-maximal inhibitory concentration (IC 50 ), the inhibition constant ( K i ), the half-maximum effective concentration (EC 50 ), and the dissociation constant ( K d ) are commonly employed. 11 − 13 However, IC 50 and EC 50 are theoretically assay-specific measurements, and so in contrast to equilibrium constants, these values are comparable only when the data are from compounds in a single assay. Nevertheless, several studies have used mixed IC 50 data for QSAR model construction because of the limited numbers of data points from single assays, and such QSAR models have sometimes shown satisfactory predictive ability.…”

Section: Introductionmentioning

confidence: 99%

Ranking-Oriented Quantitative Structure–Activity Relationship Modeling Combined with Assay-Wise Data Integration

2021

View full text Add to dashboard Cite

In ligand-based drug design, quantitative structure–activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC 50 ). Experimental IC 50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

show abstract

Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

Cited by 20 publications

References 40 publications

QSAR studies of the antioxidant activity of anthocyanins

QSAR studies of the antioxidant activity of anthocyanins

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

Ranking-Oriented Quantitative Structure–Activity Relationship Modeling Combined with Assay-Wise Data Integration

Contact Info

Product

Resources

About