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Quantitative structure-property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like Q²(F1), Q²(F2), Q²(F3), CCC, and r²(m) (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, r²(m) shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, r²(m) provides the most stringent criterion (especially with Δr²(m) at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes.

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 r(m)(2) 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 r(m)(2). Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the r(m)(2) metrics has been introduced here. The present study reports that the web application can be easily used for computation of r(m)(2) metrics provided observed and QSAR-predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to r(m)(2) calculation.

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Interleukin-6 (IL-6) signaling network has been implicated in oncogenic transformations making it attractive target for the discovery of novel cancer therapeutics. In this study, potent antiproliferative and apoptotic effect of diacerein were observed against breast cancer. In vitro apoptosis was induced by this drug in breast cancer cells as verified by increased sub-G1 population, LIVE/DEAD assay, cell cytotoxicity and presence of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells, as well as downregulation of antiapoptotic proteins Bcl-2 and Bcl-xL and upregulation of apoptotic protein Bax. In addition, apoptosis induction was found to be caspase dependent. Further molecular investigations indicated that diacerein instigated apoptosis was associated with inhibition of IL-6/IL-6R autocrine signaling axis. Suppression of STAT3, MAPK and Akt pathways were also observed as a consequence of diacerein-mediated upstream inhibition of IL-6/IL-6R. Fluorescence study and western blot analysis revealed cytosolic accumulation of STAT3 in diacerein-treated cells. The docking study showed diacerein/IL-6R interaction that was further validated by competitive binding assay and isothermal titration calorimetry. Most interestingly, it was found that diacerein considerably suppressed tumor growth in MDA-MB-231 xenograft model. The in vivo antitumor effect was correlated with decreased proliferation (Ki-67), increased apoptosis (TUNEL) and inhibition of IL-6/IL-6R-mediated STAT3, MAPK and Akt pathway in tumor remnants. Taken together, diacerein offered a novel blueprint for cancer therapy by hampering IL-6/IL-6R/STAT3/MAPK/Akt network.

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 ...

Validation is a crucial aspect for quantitative structure-activity relationship (QSAR) model development. External validation is considered, in general, as the most conclusive proof of predictive capacity of a QSAR model. In the absence of truly external data set, external validation is usually performed on test set compounds, which are members of the original data set but not used in model development exercise. In the case of small data sets, QSAR researchers experience problem in model development due to the fact that the developed models may be less reliable on account of the small number of training set compounds and such models may also show poor external predictability because the models may not have captured all necessary features required for the particular structure-activity relationships. The present paper attempts to show that 'true r 2 m (LOO) ' statistic calculated based on the model derived from the undivided data set with application of variable selection strategy at each cycle of leave-one-out (LOO) validation may reflect external validation characteristics of the developed model thus obviating the requirement of splitting of the data set into training and test sets. This approach may be helpful in the case of small data sets as it uses all available data for model development and validation thus making the resulting model more reliable.

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery.