A chain of π-stacked molecules in 4-(2-chlorophenyl)pyrrolo[1,2-a]quinoxaline and a hydrogen-bonded sheet in (4RS)-4-(1,3-1,3-benzodioxol-6-yl)-4,5-dihydropyrrolo[1,2-a]quinoxaline

Synthesis of Biologically Active Molecules through Multicomponent Reactions

Focusing on the literature progress since 2002, the present review explores the highly significant role that multicomponent reactions (MCRs) have played as a very important tool for expedite synthesis of a vast number of organic molecules, but also, highlights the fact that many of such molecules are biologically active or at least have been submitted to any biological screen. The selected papers covered in this review must meet two mandatory requirements: (1) the reported products should be obtained via a multicomponent reaction; (2) the reported products should be biologically actives or at least tested for any biological property. Given the diversity of synthetic approaches utilized in MCRs, the highly diverse nature of the biological activities evaluated for the synthesized compounds, and considering their huge structural variability, much of the reported data are organized into concise schemes and tables to facilitate comparison, and to underscore the key points of this review.

An alternative to the goodness of fit

An alternative measure to the goodness of fit (GoF) is developed and applied to experimental data. The alternative goodness of fit squared (aGoFs) demonstrates that the GoF regularly fails to provide evidence for the presence of systematic errors, because certain requirements are not met. These requirements are briefly discussed. It is shown that in many experimental data sets a correlation between the squared residuals and the variance of observed intensities exists. These correlations corrupt the GoF and lead to artificially reduced values in the GoF and in the numerical value of thewR(F2). Remaining systematic errors in the data sets are veiled by this mechanism. In data sets where these correlations do not appear for the entire data set, they often appear for the decile of largest variances of observed intensities. Additionally, statistical errors for the squared goodness of fit, GoFs, and the aGoFs are developed and applied to experimental data. This measure shows how significantly the GoFs and aGoFs deviate from the ideal value one.

More about residual values

The usual residual values are complemented by expectation values based solely on the experimental data and the number of model parameters. These theoreticalRvalues serve as benchmark values when all of the basic assumptions for a least-squares refinement,i.e.no systematic errors and a fully adequate model capable of describing the data, are fulfilled. The prediction ofRvalues as presented here is applicable to any field where model parameters are fitted to data with known precision. For crystallographic applications,F2-based residual benchmark values are given. They depend on the first and second moments of variance, intensity and significance distributions, 〈σ2〉, 〈Io2〉, 〈Io2/σ2〉. Possible applications of the theoreticalRvalues are, for example, as a data-quality measure or the detection of systematic deviations between experimental data and model predicted data, although the theoreticalRvalues cannot identify the origin of these systematic deviations. The change inRvalues due to application of a weighting scheme is quantified with the theoreticalRvalues.