Indrani Mitra

On Two Novel Parameters for Validation of Predictive QSAR Models

Validation is a crucial aspect of quantitative structure–activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q2 for internal validation and predictive R2 for external validation) may be supplemented with two novel parameters rm2 and Rp2 for a stricter test of validation. The parameter rm2(overall) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter Rp2 penalizes model R2 for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of rm2 parameter, rm2(LOO) and rm2(test), penalize a model more strictly than Q2 and R2pred respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q2 and R2pred) but fail to achieve the required values for the novel parameters rm2 and Rp2. Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data

Quantitative structure–activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The rm2 metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression‐based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of rm2. Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the rm2 metrics has been introduced here. The present study reports that the web application can be easily used for computation of rm2 metrics provided observed and QSAR‐predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to rm2 calculation.

Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants

In the present work, quantitative structure–activity relationship (QSAR) models have been built for a wide variety of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants, with their Trolox equivalent antioxidant capacity measured using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid) radical (ABTS√+) assay methods. Non-linear models obtained using genetic partial least-squares technique were acceptable both in terms of internal and external predictivity. Validation of developed models using metrics and randomisation technique yielded results indicating the predictivity and robustness, respectively, of the developed models. The models signify that the presence of ketonic oxygen within the molecular structure favours their antioxidant activity. In addition, the number of hydroxyl groups, extent of branching, degree of methoxylation and the number of methyl and methylene substituents also dictate the antioxidant activity of these molecules. Thus, the QSAR models developed here can be utilised for the antioxidant activity prediction of a new series of molecules.

On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

Quantitative structure-activity relationship modeling of antioxidant activities of hydroxybenzalacetones using quantum chemical, physicochemical and spatial descriptors.

We have modeled antioxidant activities of hydroxybenzalacetones against lipid peroxidation induced by t‐butyl hydroperoxide (pC1), γ‐irradiation (pC2) and also their 1,1‐diphenyl‐2‐picryl hydrazyl (DPPH) free radical scavenging activity (pC3) using quantitative structure–activity relationship technique. The quantitative structure–activity relationship models were developed using different statistical methods like stepwise multiple linear regression, genetic function approximation and genetic partial least squares with descriptors of different categories (quantum chemical, physicochemical, spatial and substituent constants). The models were validated by internal validation and randomization techniques. The model predictivity was judged on the basis of their cross‐validated squared correlation coefficient (Q2) and modified r2 () values. The best models for the two responses, pC1 and pC2, were obtained by genetic partial least squares technique while the best model for the third response, pC3, was obtained by genetic function approximation technique. The developed models suggest that the distribution of charges on the phenolic nucleus and the phenolic oxygen as well as the charged surface areas of the molecules together with the geometry and orientation of the substituents significantly influence all the three types of responses (pC1, pC2 and pC3). The developed models may be used to design hydroxybenzalacetones with better antioxidant activities.

Pharmacophore mapping of arylamino-substituted benzo[b]thiophenes as free radical scavengers

AbstractPredictive pharmacophore models have been developed for a series of arylamino-substituted benzo[b]thiophenes exhibiting free radical scavenging activity. 3D pharmacophore models were generated using a set of 20 training set compounds and subsequently validated by mapping 6 test set compounds using Discovery Studio 2.1 software. Further model validation was performed by randomizing the data using Fischer’s validation technique at the 95% confidence level. The most predictive pharmacophore model developed using the conformers obtained from the BEST method showed a correlation coefficient (r) of 0.942 and consisted of three features: hydrogen bond donor, hydrogen bond acceptor and aromatic ring. Acceptable values of external validation parameters, like

Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols.

R_{{\rm{pred}}}^2

Design, synthesis and exploring the quantitative structure–activity relationship of some antioxidant flavonoid analogues

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