Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer's disease: an in silico approach

Ambure, Pravin; Roy, Kunal

doi:10.1039/c3ra46861e

Cited by 22 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this purpose, we have chosen 6 published/previously used data sets for developing predictive in silico QSAR models towards various endpoints. The first data set represents toxicity of 306 ionic liquids towards Vibrio fischeri , the second set is on odor threshold of 86 wine components, the third set deals with sweetness potency of 240 organic molecules, the fourth set is on 224 cyclin‐dependent kinase 5/p25 (CDK5/p25) inhibitors, the fifth set represents acetylcholinesterase (AChE) inhibitory activity of 426 functionalized organic chemicals, and the final set is a data set comprising solubility of C 60 in 156 organic solvents . All 6 data sets were first rationally divided into respective training and test sets using 3 different techniques, namely, sorted response (Division 1), Kennard‐Stone algorithm (Division 2), and modified k ‐medoids clustering (Division 3) using 2 software tools, namely, Dataset Division version 1.2 (for sorted response and Kennard‐Stone algorithm‐based divisions) and Modified k‐Medoid version 1.2 developed by us .…”

Section: Methodsmentioning

confidence: 99%

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

Self Cite

107

View full text Add to dashboard Cite

Quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/ QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an "intelligent" selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_ Tools/ and http://dtclab.webs.com/software-tools.QSAR models (based on OECD guidelines 4 ) can be used to "generate" data which can be used for regulatory decisions. The importance of QSAR has also greatly enhanced in recent years due to their potential application in challenging areas like modeling of responses for chemical mixtures, bioactive peptides, nanomaterials, and others. 5 The accuracy and reliability of predictions of QSAR models are very important in their application in regulatory decision support process. Validation is the method which checks reliability and precision of predictions of QSAR models. 6 Although, several techniques including cross-validation, Y-scrambling, and test set validation are commonly employed, in general, external validation is considered as the gold standard for checking predictive ability of QSAR models. 7 However, some group of scientists think cross-validation is better suited for checking predictive ability of QSAR models in order to avoid loss of information from splitting of the data set into training and test sets. 8 They have also argued that the test of predictive ability of QSAR models from a single training-test split is biased and insufficient. 8 Although a comparison of suitability of cross-validation vs external validation for judging predictive ability of QSAR models is a matter of debate, the importance of a test to check quality of predictions ...

show abstract

Section: Methodsmentioning

confidence: 99%

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

Self Cite

107

View full text Add to dashboard Cite

show abstract

“…Ambure and Roy [121] developed 2D-QSAR, group-based QSAR (G-QSAR) and quantitative activity-activity relationship (QAAR) models based on a congeneric series of 224 cyclin-dependant kinase 5/p25 (CDK5/p25) inhibitors (Fig. 31 ) to explore structural features needed for CDK5/p25 inhibition considering activity against CDK5/p25 and selectivity over CDK2.…”

Section: Qsar Studymentioning

confidence: 99%

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Makhouri

Ghasemi

2018

View full text Add to dashboard Cite

show abstract

“…Ambure and Roy [38] examined a congeneric series of cyclin-dependent kinase 5/p25 (CDK5/p25) inhibitors to understand the structural requirements for improving activity against CDK5/p25 and selectivity over CDK2 by the development of 2D-QSAR, G-QSAR, and quantitative activityÀactivity relationship (QAAR) models. The 2D-QSAR and G-QSAR models explore the probable structural requirements for improving activity, while the QAAR model assists the improved understanding of the features required for selectivity of the inhibitors.…”

Section: Mathematical Correlation To Activity Predictionmentioning

confidence: 99%

Newer QSAR Techniques

Roy

Kar

Das

2015

Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment

Self Cite

View full text Add to dashboard Cite

Exploring structural requirements of leads for improving activity and selectivity against CDK5/p25 in Alzheimer's disease: an in silico approach

Abstract: A congeneric series of 224 cyclin-dependant kinase 5/p25 (CDK5/p25) inhibitors was exploited to understand the structural requirements for improving activity against CDK5/p25 and selectivity over CDK2.

Cited by 22 publications

References 24 publications

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Newer QSAR Techniques

Contact Info

Product

Resources

About