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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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The principles of green chemistry (GC) can be comprehensively implemented in green synthesis of pharmaceuticals by choosing no solvents or green solvents (preferably water), alternative reaction media, and consideration of one-pot synthesis, multicomponent reactions (MCRs), continuous processing, and process intensification approaches for atom economy and final waste reduction. The GC's execution in green synthesis can be performed using a holistic design of the active pharmaceutical ingredient's (API) life cycle, minimizing hazards and pollution, and capitalizing the resource efficiency in the synthesis technique. Thus, the presented review accounts for the comprehensive exploration of GC's principles and metrics, an appropriate implication of those ideas in each step of the reaction schemes, from raw material to an intermediate to the final product's synthesis, and the final execution of the synthesis into scalable industry-based production. For real-life examples, we have discussed the synthesis of a series of established generic pharmaceuticals, starting with the raw materials, and the intermediates of the corresponding pharmaceuticals. Researchers and industries have thoughtfully instigated a green synthesis process to control the atom economy and waste reduction to protect the environment. We have extensively discussed significant reactions relevant for green synthesis, one-pot cascade synthesis, MCRs, continuous processing, and process intensification, which may contribute to the future of green and sustainable synthesis of APIs. CONTENTS1. Introduction 3638 2. Green Chemistry (GC) 3639 2.1. Definition 3639 2.2. The Principles of GC 3641 2.3. The Metrics of GC 3641 2.3.1. Standardization of the E Factor Concept 3641 2.3.2. Baran's Process Ideality Metric 3641 2.3.3. Identification of Process Complexity 3641 2.3.4. The Green Aspiration Level Concept 3642 2.3.5. Innovation Green Aspiration Level (iGAL) 3642 3. The Choice of Solvents in the Green Synthesis of APIs 3642 4. The Reaction with Green Solvents 3643 4.1. The No-Solvent Process 3644 4.