Unification of reaction metrics for green chemistry II: Evaluation of named organic reactions and application to reaction discovery

The concept of minimum atom economy (AE)(min) and maximum environmental impact factor E-max is introduced and applied to a database of more than 400 named organic reactions by employing generalized Markush structures as a means of gauging worst-case scenarios for waste production for chemical reactions. The scope of the method can be extended to evaluate green performances for any chemical reaction once all byproducts are identified. From the inverse relationship connecting AE and E-mw (environmental impact factor based on molecular weight) and an analogous one connecting RME (reaction mass efficiency) and E (Sheldon environmental impact factor), a minimum value of AE or RME equal to the golden ratio, 0.618 (or 61.8%), is suggested as a threshold metric for gauging the true greenness of reactions. The rationale for this is that this condition is met when the magnitude of AE exceeds that of E-mw and similarly when RME exceeds E. Probabilities for achieving this condition are also determined for several organic reactions, and general expressions for evaluating such probabilities as functions of a general threshold alpha between 0 and 1 are also derived. Reactions in the database are classified according to general reaction types, and each class is ranked according to the golden threshold metric. Patterns with respect to atom economical efficiency revealed by this analysis are discussed, including the introduction of expressions for determining the viability of recycling waste byproducts back to reagents. It is shown that the database can be used in a diversity-oriented approach to discover new multicomponent reactions (MCRs) by combinatorial analysis of Markush fragments. In this context the top seven most frequently appearing Markush structures in the database yield 33 viable three-component MCRs of which 12 have literature precedence. Synthetic strategies for reaction design are put forward using the optimum criteria suggested by analysis of the database.