Abstract
A computational approach using density functional theory to compute the energies of the possible σ-complex reaction intermediates, the “σ-complex approach”, has been shown to be very useful in predicting regioselectivity, in electrophilic as well as nucleophilic aromatic substitution. In this article we give a short overview of the background for these investigations and the general requirements for predictive reactivity models for the pharmaceutical industry. We also present new results regarding the reaction rates and regioselectivities in nucleophilic substitution of fluorinated aromatics. They were rationalized by investigating linear correlations between experimental rate constants (k) from the literature with a theoretical quantity, which we call the sigma stability (SS). The SS is the energy change associated with formation of the intermediate σ-complex by attachment of the nucleophile to the aromatic ring. The correlations, which include both neutral (NH3) and anionic (MeO−) nucleophiles are quite satisfactory (r = 0.93 to r = 0.99), and SS is thus useful for quantifying both global (substrate) and local (positional) reactivity in SNAr reactions of fluorinated aromatic substrates. A mechanistic analysis shows that the geometric structure of the σ-complex resembles the rate-limiting transition state and that this provides a rationale for the observed correlations between the SS and the reaction rate.
Highlights
BackgroundComputational chemistry has become an indispensible tool for medicinal chemists, biologists and pharmacologists throughout all stages of the pharmaceutical research process
It seems that the level of theory employed, B3LYP/6-31G(d,p), is sufficient for the present purpose, which is to make semiquantitative rankings of substrate and positional reactivity between species that are run under the same reaction conditions
It is important to note that the sigma stability (SS) values do not give an explicit indication of absolute energy barriers, and the approach is limited to reactivity comparisons where species are run under the same reaction conditions
Summary
Computational chemistry has become an indispensible tool for medicinal chemists, biologists and pharmacologists throughout all stages of the pharmaceutical research process. One application is in the selection or virtual screening of the, in many cases, numerous possible alternative synthetic routes to a target molecule, e.g., a candidate drug. To this end, efficient models that enable the prediction of product selectivity and relative reaction rates in a quantitative or semiquantitative way would be highly valuable. As a complement to experimental work, the use of such tools can help to minimize “trial and error” experimenta-.
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